Forma y desempeño de las normas internacionales de contabilidad

In a discussion paper published by RAND Health Joanne Lynn and David Adamson review the changing demographic of American’s health and the growing pressure on health services of chronic disease. The graphic shown in Figure 2.12 (on page 85) is used to illustrate the care needs of those elderly people sick enough to die (Lynn J and Adamson D 2003). Writing for the College of Physicians (UK) Felicity Murtagh used the trajectory of decline graphs to illustrate variance in the illness trajectories according to the patient’s

Page 88 of 242 diagnosis (Murtagh F, Preston M et.al. 2004). The heart failure trajectory is described by Goldstein and Lynn (Goldstein N and Lynn J 2006) in regard to planning care and policy change (Figure 2.14 below).

Figure 2.14 from Goldstein and Lynn (2006 p12): Typical trajectory of disease for patients with cancer and heart failure

Freedman used the trajectories of decline in developing a framework for identifying the effect of interventions on late life disablement (Figure 2.15 below).

Figure 2.15 From Freedman et.al. 2006 (p496): Illustration of three prototypical trajectories of the disablement process

This paper reviews the literature for interventions to avoid the rapid decline phase of the three trajectories by either avoiding onset of pathology leading to

Page 89 of 242 disability; slowing the progression of disease; and strategies to restore function and autonomy. This is similar to the disablement process described by

Verbrugge in Section 2.6.2 (on page 100).

Three studies have identified different trajectories of functional limitation for community living elders suffering from dementia.

Figure 2.16 From (Nikolova R, Demers L et.al. 2009, p30): Differences in functional limitation related to severity of cognitive decline over time

The study by Nikolova (Figure 2.16 above) identified four cognitive

trajectories associated with functional limitation whereas Dodge (Dodge H, Du Y et.al. 2006 and McConnell (McConnell E, Branch L et.al. 2003) identified three trajectories of functional decline in people with dementia. These studies were done in community living elders and the oldest subjects were in the trajectories with the most marked decline.

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Figure 2.17 From Murray et.al. 2005: Typical illness trajectories for people with progressive chronic illness. Adapted from Lynn and Adamson 2003

Murray in the UK has used the trajectory of decline to describe the service use of patients dying with cancer in comparison to other disease conditions (Murray SA, Boyd K et.al. 2002; Murray S, Kendall M et.al. 2005). The graph depicted in this paper (Figure 2.17) has been ‘adapted’ from another publication by Joanne Lynn in 2003 (Lynn J and Adamson D 2003).

What emerges from this review of the use of the trajectory of decline model in the literature following the first publication of the graphs by Joanne Lynn in 2001, is that the trajectory of decline model has a broad application and appeal not just in aged care. For example in response to the article published by Murray (2005) in the BMJ an intensivist in NZ wrote:

Finally, I have been searching for a graphical way to convey the concept that since critical illness often leads to death and prediction of individual outcome in intensive care.

Stephen Streat, in BMJ ‘Rapid Responses’ to Murray et.al. BMJ April 2005.

These figures, attributed to Joanne Lynn have appeared in the Australian Government ‘Guidelines for a Palliative Approach in Residential Aged Care’ (2004, p139) shown in Figure 1.1 (on page 27) as well as other, more recent,

Page 91 of 242 policy documents in the UK, EU and US (listed in Table 2.1 on page 83). However, it appears that although this concept arose in the earlier Glaser and Strauss literature this thread is unrelated in that there is no cross referencing between the threads until 2002. While this could be an interesting academic exercise, it becomes an effort to understand where this trajectory concept resonates (if it can be demonstrated empirically). As described by Stephen Streat above, for a clinician this concept makes sense. What was found in the course of the literature review process, and what made the review difficult at times, is that the threads for the trajectory of decline theory appear in particular literature lineages and their related publishing modalities. Glaser and Strauss for example did not publish in the BMJ or JAMA. They use monographs – that are completely invisible outside the sphere of the university library – being discovered through reading about them in the first reference to Glaser and Strauss (1968) by June Lunney in 2002. However, policy makers are not interested in academic arguments about literature lineages.

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2.5

Empirical evidence of the ‘trajectory of decline’

There are only two papers that report empirical evidence of the multiple trajectories of decline in an elderly population as articulated by Joanne Lynn and two that identify additional differences between the trajectory of cancer and renal failure for all patients not just the elderly. In Figure 1.1 attributed to Joanne Lynn, there are only three trajectories described – cancer; organ system failure; and dementia/frailty (DoHA 2004). The first research paper, that classified deceased elders based on their Medicare claim data, introduces two hitherto undescribed trajectories – sudden and ‘other’ (Lunney J, Lynn J et.al. 2002). The graphic representation of four of the trajectories from this paper is reproduced in Figure 2.18 below. There is no graph for the ‘other’ group. The cause of death was determined from the physician code on the Medicare claim, a classification method that is comparable to cause of death information from the death certificate (Hogan C, Lunney J et.al. 2001).

Figure 2.18 From Lunney, Lynn and Hogan (2002 p1109) “Proposed trajectories of dying”

Having established that an elderly population could be classified by cause of death and that there were some differences in demographics and care

expenditure before death between the groups, the second study sought to determine if the groups differed by their slope of functional decline. In this paper deceased elders were classified into their diagnostic group primarily by

Page 93 of 242 death certificate data. The measure of function (determined via interview of participant or carer) for participants in each trajectory grouping over the 12 months before death was clustered by month of interview to give a mean value of function. As shown in Figure 2.18 below, the mean function declined in all groups. The frailty group in particular had the highest ADL dependence before death (Lunney J, Lynn J et.al. 2003).

Figure 2.19 From Lunney, Lynn et.al. (2003 p2390): “Dependent activities of daily living (ADLs) for each month cohort, by trajectory group”

Unlike the 2002 study where a participant was classified into the frailty group if they had a Medicare claim for Alzheimer’s disease, dementia, delirium, Parkinson’s disease, stroke, pneumonia, dehydration, hip fracture,

incontinence, syncope, or leg cellulitis8, the 2003 methodology placed any subject with a nursing home admission into the ‘frailty’ trajectory. This was important because hospital deaths from cancer declined from 1980 with an increasing number of people dying at home by 1998 (38%), and 17% occurring

8 _{Classification based on analysis from the large USA HMO Kaiser Permanente (Haan et.al} 1997)

Page 94 of 242 in nursing homes (Flory J, Young-Xu Y et.al. 2004). Hence it is arguable that 17% of cancer deaths were excluded from the cancer trajectory group involved in this research because they were classified as being on the ‘frailty trajectory’ in a nursing home. The implication of this potential misclassification is that residents of nursing homes are being identified as having conditions related to frailty only and thus may be excluded from interventions targeting diseases that could directly lead to their death, such as cancer.

Comparing differences in the pattern of functional decline between people dying of cancer and other leading causes of death in adults (30 years and older) Joan Teno used a retrospective interview method with the next of kin. The measure of physical function was an estimate of the dying person’s difficulties with ADLs and mobility. As with the other studies, the measure of function for the subjects was grouped by month to give a mean score. The trajectories for each identified disease group are shown in Figure 2.20 below.

Figure 2.20 From Teno, Weitzen, Fennell and Mor (2001 p461) “Age- adjusted activities of daily living (ADLs) scores by month before death”

These graphs do not try to emulate the Lynn concept by reversing the y-axis to demonstrate a ‘decline’ in function. A worsening ADL score indicates

Page 95 of 242 increasing care needs. This study does show a difference between the cancer trajectory and other diseases. There are a few studies in the research literature that have identified an association between ADL loss and diagnosis in the elderly. In these studies of community living elders 80 years and older, the diagnosis group with diseases such as cancer and heart disease had no or minimal ADL loss. In comparison the group with health conditions associated with frailty such as stroke, cognitive impairment and fracture did have

significant ADL loss (Bootsma-van der Wiel A, de Craen A et.al. 2005; Cesari M, Onder G et.al. 2006; Takayama M, Hirose N et.al. 2007; Ferrer A, Formiga F et.al. 2008; Wu H, Flaherty J et.al. 2012). However, these were cross-

sectional cohort studies and hence only reported an association between functional loss and diagnosis not a trajectory of functional decline.

Building on the empirical research of Lunney and colleagues described above, a sixth trajectory of decline has subsequently been identified for renal failure (Murtagh F, Addington-Hall J et.al. 2011). However the population for this study was not restricted to an elderly cohort. The age range was 51 to 95 years, being selected from hospital patients with end stage renal failure. The graph shown in Figure 2.21 below used mean data grouped by month of

measurement. The same analysis method used by Lunney and colleagues (Lunney J, Lynn J et.al. 2003).

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Figure 2.21 From Murtagh, Addington-Hall and Higginson (2011 p4) “Trajectory of mean Karnofsky Performance Scale (KPS) score over the last year of life for those who die (N=46).

There does appear to be a modest decline from mean scores in the mid-60’s to low 50’s with no significant difference from 12 months to the month before death. However the dramatic downward slope is due to the Karnofsky Scale being zero for death.

The trajectory of decline graphs have been taken up by policy makers worldwide for use in a broad range of populations including nursing home residents. The two empirical papers by Lunney and colleagues (Lunney J, Lynn J et.al. 2002; Lunney J, Lynn J et.al. 2003) identify the trajectory of decline in community living US elders. Yet there is no evidence that the trajectories of decline exist in the NH population with specific, expensive and growing care needs. In the next section, the markers of ageing and the conceptual models used to describe the ageing process are reviewed in order to identify what options there are available to profile the functional changes in a nursing home population and map their trajectory.

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