Elsevier

Social Science & Medicine

Volume 153, March 2016, Pages 210-219
Social Science & Medicine

The DEP-6D, a new preference-based measure to assess health states of dependency

https://doi.org/10.1016/j.socscimed.2016.02.020Get rights and content

Highlights

  • We propose a new dependency health state classification system coined as DEP-6D.

  • DEP-6D classifies the dependency states as a combination of six dimensions.

  • The dimensions are: eat, incontinence, self-care, mobility, housework and cognition.

  • We use time trade-off questions and estimate a preference-based scoring algorithm.

  • DEP-6D scores can be used in QALY calculations and cost-utility analysis.

Abstract

In medical literature there are numerous multidimensional scales to measure health states for dependence in activities of daily living. However, these scales are not preference-based and are not able to yield QALYs. On the contrary, the generic preference-based measures are not sensitive enough to measure changes in dependence states. The objective of this paper is to propose a new dependency health state classification system, called DEP-6D, and to estimate its value set in such a way that it can be used in QALY calculations. DEP-6D states are described as a combination of 6 attributes (eat, incontinence, personal care, mobility, housework and cognition problems), with 3–4 levels each. A sample of 312 Spanish citizens was surveyed in 2011 to estimate the DEP-6D preference-scoring algorithm. Each respondent valued six out of the 24 states using time trade-off questions. After excluding those respondents who made two or more inconsistencies (6% out of the sample), each state was valued between 66 and 77 times. The responses present a high internal and external consistency. A random effect model accounting for main effects was the preferred model to estimate the scoring algorithm. The DEP-6D describes, in general, more severe problems than those usually described by means of generic preference-based measures. The minimum score predicted by the DEP-6D algorithm is −0.84, which is considerably lower than the minimum value predicted by the EQ-5D and SF-6D algorithms. The DEP-6D value set is based on community preferences. Therefore it is consistent with the so-called ‘societal perspective’. Moreover, DEP-6D preference weights can be used in QALY calculations and cost-utility analysis.

Introduction

The aging population represents one of the most important challenges developed countries must face in the upcoming years. The portion of the population aged over 65 accounted for 15.4% of the OECD average in 2010, but this percentage is likely to nearly double in 2050 (OECD, 2013). One of the most obvious consequences of an aging population is the increasing number of people who will require assistance to carry out their activities of daily living (ADLs). This is one of the greatest concerns of the adult population. It is even more important for them than the loss of their very own health (Finlayson, 2004, Quine and Morrell, 2007). This process involves increasing the burden of care within the family (informal care) and will pose a great challenge to the sustainment of the public purse —if in 2006–2010 the public expenditure on long-term care services in the OECD represented 0.8% of GDP, it is estimated to involve between 1.6% and 2.1% of GDP in 2060 (De La Maisonneuve and Martins, 2013). In any case, forecasting how the prevalence of ADL dependence will evolve is subject to great uncertainty because many factors are likely to affect its future trend. In addition to lifestyle changes that can modify risk factors, new technologies are continuously emerging. They will clearly have an impact on the levels of dependency because they either prevent or delay the onset of dependency or reduce its severity once it actually happens (preventing stroke, delaying both the onset of disease and the progression of Alzheimer's disease, rehabilitation, technical aids, education programs to prevent falls, etc.).

Within this context, it is useful to have instruments capable of measuring the changes in the severity of dependence fairly accurately to both plan social/health services, in general, and evaluate programs aiming to prevent and/or delay dependency states, in particular. Multiple scales are designed to measure the loss of independence in the ADLs within a clinical context (McDowell, 2006, Kane and Kane, 2002), such as the Katz scale (Katz et al., 1963), Barthel Index (Mahoney and Barthel, 1965) or Lawton and Brody scale (Lawton and Brody, 1969). However, despite its widespread use, these instruments are not appropriate for economic evaluations because they are not able to yield quality-adjusted life-years (QALYs), the outcome measure recommended by leading health technology assessment agencies. To estimate the number of QALYs gained from an intervention, life years are weighted by preference weights (or utilities), where zero indicates death and one good health.

A common way of assigning utilities to health-related quality of life (HRQoL) states is to use one of the existing preference-based generic instruments. Well-known examples are EQ-5D (Dolan, 1997), SF-6D (Brazier et al., 2002, Brazier and Roberts, 2004) and Health Utility Index (Feeny et al., 2002). However, one disadvantage of these instruments is that their health state descriptive system are not sensitive enough for some medical conditions (Brazier et al., 1999), and the effectiveness of interventions may be undervalued. Donaldson et al. (1988) and Chisholm et al. (1997) conclude that generic measures are, as compared to condition specific measures, less sensitive to changes in older adult health status. In particular, these generic measures were not designed to measure ADL dependency and although they could be used for this purpose (for instance EQ-5D does this to some extent on the three functioning dimensions), empirical evidence suggests these instruments may not be sensitive enough to detect significant changes in the level of ADL dependency. On the one hand, different studies found a poor relation between the EQ-5D and the Barthel index (Kaambwa et al., 2013, Van Exel et al., 2004, Hickson and Frost, 2004), “underscoring the fact that these outcome measures were designed to capture different aspects of health status” (Kaambwa et al., 2013). For instance, the loss of feeding ability involves an important change at an individual and family level, yet they would rarely produce a change in any of preference-based generic instruments commonly used. On the other hand, the loss of physical capacity is not the only dimension of ADL dependence. Decisional dependence is yet another dimension frequently pointed out in the literature (O'Shea et al., 2007). Cognitive limitations can produce loss of capacity for autonomous decision-making, which makes a person reliant on others regardless of whether or not they have the physical capacity to do the activities. This loss of decisional independence is not included in the generic instruments usually used for economic evaluation. The lower sensitivity of these instruments in detecting changes in the physical or decisional dependence could explain why groups of patients requiring very different hours of help can obtain similar scores in HRQoL generic instruments (Sandberg et al., 2015).

Hence, it would be desirable to have a richer descriptive system to optimally evaluate those health care programs that specifically aim to prolong or enhance independence. This is why some researchers (Goldstein et al., 2002, Bravata et al., 2005, Sims et al., 2008) have elicited preference weights for various ADLs dependence states based on the combination of the 6 ADLs included in the Katz scale plus an additional ADL, walking. However, these authors do not use an experimental design (they only obtain the weights of specific states) that allows them to estimate a scoring algorithm capable of predicting all the possible health states for dependence in ADLs. Moreover, the severity of the dependence in each of the 7 ADLs is not graded (it only considers whether or not help is required to perform each ADL). Lastly, preferences were elicited from a convenience sample (older adult members of the Kaiser Permanente Medical Care Program of Northern California) rather than from the general population.

Other authors (Ryan et al., 2006, Coast et al., 2008) have developed instruments related to dependence states for the evaluation of health care and social services interventions for older people. In the first case, domains and levels of their instrument (OPUS) were designed to reflect whether needs relevant to social services clients are met (e.g. whether their home is clean and comfortable) rather than capture changes in functional status (e.g. whether dependency lowers with rehabilitation) as, indeed, is our aim. The ICECAP capability index for older people developed by Coast et al. (2008) does not assess preferences but capabilities, and its scores are anchored on an “absence of capability”-“full capability” scale. The instrument introduced in our paper, on the contrary, provides classical QALYs anchored on a death-full health scale as those commonly used in cost-utility analysis. Therefore, both approaches are complementary rather than mutually exclusive.

This paper aims to propose a new dependency health state classification system, called DEP-6D, and estimate its value set so it can be used in QALY calculations. This instrument could be included within the domain-specific measurement scales where the dominance to be evaluated is the level of dependence in the ADL. Although there are different methods to obtain QALY, we followed the guidelines from NICE (Brazier and Rowen, 2012) to design specific instruments, that is “preference-based measures derived from validated measures of HRQoL, with the value set obtained from the general population preferably using techniques similar to the protocol used to obtain the EQ-5D value set”. Following these guidelines, first, we propose a new dependency health state classification system based on items usually reported in the functional disability indexes, plus an additional item, which recalls levels of dependence related to cognitive problems that these traditional disability indexes do not consider. Second, we estimate the DEP-6D scoring algorithm from a set of direct measurements of dependency states performed with the time trade-off (TTO) from a general population sample.

Section snippets

The DEP-6D dependency health states classification system

The process to design the descriptive component of the DEP-6D instrument is detailed in Electronic supplementary material [INSERT LINK TO ELECTRONIC SUPPLEMENTARY MATERIAL]. The resulting selection of dimensions/attributes is showed in Table 1. DEP-6D states are described as a combination of 6 attributes (eat, incontinence, personal care, mobility, housework and cognition/mental problems), with 3 or 4 levels each. The first four dimensions assess the level of performance of basic ADLs and they

Sample

Table 3 shows the main characteristics of respondents and the general population from which the sample was taken. The sample is representative of the general population in terms of sex and habitat (χ2 test at the 5% level), but not in terms of age (t-test at the 5% level), education and income (χ2 test at the 5% level).

Direct dependency state valuations

Table 4 shows mean utilities for the 24 states directly valued. For the overall sample, each dependency state was valued on average by 78 individuals, ranging from a minimum of

Discussion

This paper reports the estimation of a preference-based scoring algorithm for a new dependency health state classification system coined as DEP-6D. In the DEP-6D instrument, each possible condition of daily dependence is characterized as a combination of 6 dimensions with 3 or 4 levels each. The model estimated for the DEP-6D allows to generate preference scores or utilities for a wide range of states of dependency. The interpretation given to this is akin to the one given to the scores

Acknowledgments

Financial support from Spanish Ministry of Science and Innovation (ECO2015-69334-R), Regional government of Galicia (10SEC300038PR and ECOBAS [AGRUP2015/08]), and Caixa Galicia Foundation is gratefully acknowledged. The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing, and publishing the report. We are also very grateful to researchers Lina Sofia de Matos Lourenço-Gomes (University of Trás-os-Montes and Alto Douro) and María Loureiro

References (58)

  • M. Ryan et al.

    Using discrete choice experiments to estimate a preference-based measure of outcome—an application to social care for older people

    J. health Econ.

    (2006)
  • T. Sims et al.

    Simple counts of ADL dependencies do not adequately reflect older adults' preferences toward states of functional impairment

    J. Clin. Epidemiol.

    (2008)
  • G.W. Torrance

    Measurement of health state utilities for economic appraisal: a review

    J. health Econ.

    (1986)
  • J.M. Abellán Perpiñán et al.

    Lowering the ‘floor’of the sf-6d scoring algorithm using a lottery equivalent method

    Health Econ.

    (2012)
  • X. Badia et al.

    A comparison of United Kingdom and Spanish general population time trade-off values for EQ-5D health states

    Med. Decis. Mak.

    (2001)
  • H. Bleichrodt et al.

    Resolving inconsistencies in utility measurement under risk: tests of generalizations of expected utility

    Manag. Sci.

    (2007)
  • H. Bleichrodt et al.

    The validity of QALYs an experimental test of constant proportional tradeoff and utility independence

    Med. Decis. Mak.

    (1997)
  • H. Bleichrodt

    A new explanation for the difference between time trade-off utilities and standard gamble utilities

    Health Econ.

    (2002)
  • D.M. Bravata et al.

    Invariance and inconsistency in utility ratings

    Med. Decis. Mak.

    (2005)
  • J. Brazier et al.

    A checklist for judging preference-based measures of health related quality of life: learning from psychometrics

    Health Econ.

    (1999)
  • J.E. Brazier et al.

    The estimation of a preference-based measure of health from the SF-12

    Med. care

    (2004)
  • J. Brazier et al.

    NICE DSU Technical Support Document 8: An Introduction to the Measurement and Valuation of Health for NICE Submissions

    (2011)
  • J.E. Brazier et al.

    NICE DSU Technical Support Document 11: Alternatives to EQ-5D for Generating Health State Utility Values. 2011

    (2012)
  • D. Chisholm et al.

    QALYs and mental health care

    Soc. psychiatry psychiatric Epidemiol.

    (1997)
  • M.D. Clark et al.

    Discrete choice experiments in health economics: a review of the literature

    Pharmacoeconomics

    (2014)
  • C. De La Maisonneuve et al.

    Public spending on health and long-term care: a new set of projections

    OECD Econ. Policy Pap.

    (2013)
  • N.J. Devlin et al.

    Logical inconsistencies in survey respondents' health state valuations-a methodological challenge for estimating social tariffs

    Health Econ.

    (2003)
  • N. Devlin et al.

    An EQ-5D-5L Value Set for England

    (2015)
  • P. Dolan

    Modeling valuations for EuroQol health states

    Med. care

    (1997)
  • Cited by (0)

    View full text