Available via license: CC BY 4.0
Content may be subject to copyright.
Nathan Theill, PhDa,b,c; Denis Gerstorf, PhDd; Stefanie Eicher, PhDa,b,e; Heike Geschwindner, PhDf; Christina Röcke,
PhDa,b,g, Mike Martin, PhDa,b,e,g; Henrike Wolf, MDc,h & Florian Riese, MDa,c*
Similar dynamics of terminal functional decline in nursing home residents with and
without dementia
a
University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland.
b
Center for Gerontology, University of Zurich, Zurich, Switzerland.
c
Department of Geriatric Psychiatry, University Hospital of Psychiatry, Zurich, Switzerland.
d
Institute of Psychology, Humboldt University, Berlin, Germany.
e
Department of Psychology, University of Zurich, Zurich, Switzerland.
f
City of Zurich Nursing Homes, Zurich, Switzerland.
g
Healthy Longevity Center, University of Zurich, Switzerland.
h
Ambulatory Psychiatric Services, Psychiatrische Dienste Graubünden, St. Moritz, Switzerland.
*email: florian.riese@bli.uzh.ch
Received: 2023-01-17; Accepted: 2023-03-15
DOI: 10.52095/gpa.2023.6282.1067
Abstract
Background: is study investigates the functional health trajectories at the end-of-life in nursing home residents with no
dementia, mild-to-moderate dementia, and severe dementia.
Methods: 45,803 deceased residents (mean age 87.49ys ± 7.14ys, 67.6% female, no dementia (N=18,993), mild-to-moderate
dementia (N=14,687), and severe dementia (N=12,123)) from 357 nursing homes across Switzerland were included in this
retrospective cohort study. Activities of daily living (ADL) scores of the Resident Assessment Instrument – Minimum Dataset
(RAI-MDS) were used to assess functional health. Multi-phase growth models spanning 24 months prior to death were
calculated as a function of dementia status and severity.
Results: e functional health trajectories follow a nonlinear pattern with a long period of mild decline with a mean ADL score
change of -0.118 points per months (95% CI -0.122 to -0.114) for the no dementia group, followed by a signicant terminal
drop (mean ADL change of -1.528, 95% CI -1.594 to -1.462) two to three months before death (transition point at -2.221, 95%
CI -2.306 to -2.136). Residents with dementia had a steeper preterminal decline (-0.026, 95% CI -0.32 to -0.20 for mild-to-
moderate dementia, - 0.056, 95% CI -0.062 to -0.051 for severe dementia) and less terminal decline (0.274, CI 0.211 to 0.337
for mild dementia, -0.230 to 0.336 for severe dementia). However, the transition point and the pattern of decline were similar
across the dementia groups, though proceeding at dierent levels.
Conclusion: e dynamics of terminal functional health decline in nursing home residents with and without dementia are
similar.
Keywords
End-of-life, Trajectories, Terminal decline, Nursing home, Long-term care, RAI-MDS, Dementia, Functional health
© Riese2023. This is an open access article licensed under the Creative Commons Attribution-NonCommercial-NoDerivs
License (http://creativecommons.org/licenses/by-nc-nd/3.0/).
GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
INTRODUCTION
An increasing number of people are dying with
Alzheimer’s and other dementias, many of them
in long-term care facilities (Badrakalimuthu and
Barclay 2014). Although dementia is considered
a terminal illness, little is known about the causes
and dynamics of dying with dementia (Mitchell
et al. 2009; van der Steen 2010). Knowledge about
end-of-life health trajectories (see recent systematic
review (Cohen-Manseld et al. 2018) is important
for patients and their relatives and could support the
recognition of the terminal phase and appropriate
care planning. Various approaches exist to dene
and quantify health status.
A common approach in end-of-life research is the
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GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
concept of functional health, which focuses on the
most relevant functional abilities for everyday life
of a person and is usually measured by dependency
in activities of daily living scales (ADLs) (Morris
et al. 1999; Lee et al. 2009; Hjaltadóttir et al. 2011;
Vossius et al. 2018). Dependency in ADLs is a key
feature of dementia and common in long-term care.
As a result, specialised ADL scales were developed
for this population (Morris et al. 1999). In addition,
low-functional health has repeatedly been reported
as one of the key factors for institutionalisation
and mortality in both individuals with and without
dementia (Abicht-Swensen and Debner 1999;
Flacker and Kiely 2003; Porock et al. 2005; Gaugler
et al. 2007; Lee et al. 2009; Hjaltadóttir et al. 2011;
omas et al. 2019; Nuutinen et al. 2019). In people
without dementia, trajectories of functional health
typically follow a nonlinear pattern with accelerated
decline in the last months of life, albeit with some
dierences depending on the condition or disease
(Teno et al. 2001; Lunney et al. 2003; Chen et al.
2007; Gill et al. 2010).
In dementia, the terminal phase is usually associated
with lower levels of functional health and has been
described as ‘progressive dwindling’ (Murray et
al. 2005; van der Steen 2010), but very few studies
examined and quantied patterns of functional
decline in dementia (Chen et al. 2007; Gill et al. 2010).
ey have found rather distinct end-of-life trajectories
with persistently poor functional health during the
last year of life and much less (Chen et al. 2007) or
even absence (Gill et al. 2010) of terminal decline.
However, in both previous studies the dementia group
has been restricted to cases with severe dementia, so
there is no information about trajectories of people
dying with mild or moderate dementia. In addition,
the exact pattern of trajectories and onset point of
terminal decline are not known for both populations
with and without dementia. is study aims to
close a gap in knowledge by studying trajectories of
functional health in a large sample of Swiss nursing
home residents. In a retrospective cohort study, we
modelled the trajectories of functional health, as
measured by ADL function, from 24 months prior
to death as a function of dementia status and severity
(mild-to-moderate versus severe).
METHODOLOGY
Study population and data source
is retrospective cohort study used routine healthcare
data of the Swiss version of the Resident Assessment
Instrument – Minimum Data Set (RAI-MDS) V2.0
(Morris et al. 1995; Anliker and Bartelt 2015) of
105,834 nursing home residents in Switzerland, with
cohorts for the years 1998 to 2014. Data was available
for 357 nursing homes out of 16 of 26 cantons across
Switzerland, representing about two-thirds of the
eligible nursing homes using the RAI-MDS (Anliker
and Bartelt 2015). e dataset was arranged by the
local distribution and administration company of the
RAI system, Q-Sys AG, St. Gallen, aer obtaining the
anonymised data from each participating nursing
home. As the analysis was based on anonymous
routine care data, no approval from the local ethics
committee was required (cantonal ethics committee
Zurich declaration of no objection 103-2015, KEK-
ZH-Nr. 2012-0102). is dataset has been used in a
previous publication on cognitive trajectories (Hülür
et al. 2019).
For this study, deceased residents aged ≥ 65 with
at least one RAI-MDS assessment in their last 24
months of life were included (N = 53,424). Residents
suering from disabilities with persistently high
dependency levels such as cerebral palsy (N =
192), paraplegia (N = 202), hemiplegia (N = 2,599),
quadriplegia (N = 239), or limb amputation (N =
275) were excluded from analysis, as well as those
receiving tracheostomy care (N = 167) and comatose
residents (N = 76) (total N = 3,295). Lastly, those
residents with missing values in any of the predictor
variables (dementia diagnosis (N = 3,889) or any
of the covariates in the full model (N= 4,164), were
excluded from analysis (total N = 4,326).
Instruments and measures
e RAI-MDS was developed to improve the quality
of care in long-term care in the US (Morris et al.
1990). It has become a widely used instrument for
care planning and reimbursement that is applied
in a large number of countries around the world,
including Switzerland.
e Swiss version of RAI-MDS V2.0 (Anliker et
al. 2007) is used by approximately one-third of the
nursing homes in Switzerland (Anliker and Bartelt
2015). e RAI-MDS shows high levels of reliability
for most of the MDS items, in particular for the
ADL domains (Hawes et al. 1995; Sgadari et al. 1997;
Morris et al. 1999; Poss et al. 2008). e assessments
include information on a variety of residents’ health
characteristics (for example, disease diagnoses,
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GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
functional health, cognition), and are completed by
specialised clinical professionals and physicians. A
full assessment extends over a period of two weeks
and is completed at admission and every subsequent
year or whenever care needs change signicantly.
Furthermore, an abbreviated assessment is
performed six months aer each full assessment.
e primary outcome of this study, functional health,
was assessed with the ADL index of the Swiss RAI-
MDS, which is a 15-points (range 4-18) scale based
on the residents’ performance in the following four
basic ADLs: bed movement, toilet use, transfer, and
eating. Each ADL is evaluated in terms of dependency
and need for help, resulting in a scale ranging from
1 (‘independent and no help needed’) to 5 (‘complete
dependency and help from 2+ persons needed’, or
‘no activity at all’) for each of the four ADLs except
eating which has a maximum score of 3 points. e
RAI-MDS ADL scales show high internal consistency
(Morris et al. 1999), which is also true for the present
ADL scale with Cronbachs α > 0.9 throughout
the dierent measurement time points. e scale
represents the residents’ dependency in ADLs, in
other words increasing ADL dependency results in
higher ADL scores. In order to better illustrate the
functional decline at the end of life we reverse coded
the scale for our analysis, in other words lower scores
now represent lower functional ability.
e classication of dementia status (no dementia,
mild-to-moderate dementia, severe dementia) was
based on the dementia diagnoses in the RAI-MDS
(in other words, the items Alzheimer’s disease or
Dementia other than Alzheimer’s disease), the ADL
index, and the Cognitive Performance Scale (CPS)
(Morris et al. 1994; Anliker et al. 2007). e CPS
is a 7-point scale (range from 0-6 with 0 = ‘intact’
and 6 = ‘very severe impairment’) to evaluate the
cognitive impairment of nursing home residents. It
has a performance similar to the Mini Mental State
Examination (MMSE (Folstein et al. 1975)) (Morris
et al. 1994; Hartmaier et al. 1995; Paquay et al. 2007).
According to the literature (van der Steen et al. 2006),
mild-to-moderate dementia was dened as having
any of the two dementia diagnoses and a score <5 on
the CPS and <10 on the ADL index in the last MDS
assessment before death, whereas severe dementia
was dened as having any of the two dementia
diagnoses with a CPS score ≥5 and an ADL score
≥10, respectively. Although ADL information was
used to dene dementia, the RAI-MDS ADL index
has been demonstrated to adequately cover change
in ADLs in both moderate and severe dementia
(Carpenter et al. 2006).
Nevertheless, we additionally tted the analytic
models with other classications of severe dementia,
which provided similar results and so are not
reported in this paper.
Statistical analysis
Following good practice in the terminal decline
literature (Gerstorf et al. 2014), multi-phase or ‘spline’
growth models (Cudeck and Klebe 2002; Singer and
Willett 2003; Cudeck and Harring 2007; Ram and
Grimm 2007) were tted for functional health over
time-to-death for the last 24 months of life. Multi-
phase growth models are comparable to linear
mixed models, which are especially appropriate for
longitudinal designs, as random (individual) eects
can be modelled in addition to the xed eects
(for example, dementia group or time to death). In
addition to that, multi-phase models allow for a free
estimation of dierent types of trajectories and, in
particular, of any existing transition or change points
between dierent trajectories. As the known end-
of-life trajectories show distinctive patterns for the
last few months of life (Lunney et al. 2003; Gill et al.
2010), the analysis included measures for each month
before death. Because residents usually are assessed
at longer time intervals and have dierent numbers
of time points, an accelerated longitudinal design was
applied, which is based on both cross-sectional and
longitudinal data. Here, the mixed models approach
is particularly appropriate because it allows handling
unbalanced designs and missing data under the
assumption of missing at random (MAR). For the
multi-phase growth models, dierent equations
are formulated for the time period before and aer
an estimated change or transition point k (see
Appendix for equations and formulas). Both a model
with several covariates and a dementia only model
were calculated (see Appendix). Although model
convergence was given for all models, the models
with covariates resulted in violation of second-order
optimality condition, which could not be eliminated
through tting process. However, eects of covariates
on the trajectories were negligible, so we only report
the results from the model without covariates.
Description of covariates, model description, and
results from the full model with covariates can be
found in the Appendix.
For the current model, individual-specic inter-
17
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
cepts at change point, β0i, change point, ti, and the
two slopes, β1i and β2i, were modelled as a function
of dementia status and severity. In addition to the
sample-level associations (xed eects, ys), the mod-
el estimates the residual unexplained individual
dierences (us) that are assumed to be multivariate
normally distributed, correlated with each other, and
uncorrelated with the residual error, eti. Despite the
large dataset with multiple observations, the models
could not be estimated with random eects for both
the slopes and the change point.
With the focus on identifying the transition to the last
phase of life it was more important for us to allow for
within-group variation in onset time of the terminal
phase. As a consequence, we decided to remove the
random eects for the slopes. e random eect
variance-covariance matrix was parameterised using
(squared) standard deviations. e models were t
with the SAS (SAS Institute Inc., Cary, NC) PROC
NLMIXED statement (Littell et al. 2006). Due to the
large sample size, signicance level was set to α =
.001.
RESULTS
e nal sample included the longitudinal data
of 45,803 deceased residents (mean age at death
87.49 ± 7.15) with a mean number of 3.03 (± 1.56)
observations per resident (range 1-11). e mean
distance from death at the last RAI-MDS assessment
was 2.71 ± 2.44 months, with 31,272 (68.3%)
residents having at least one assessment in their last
3 months of life.
A summary of the sample’s characteristics is displayed
in Table 1. All trajectories were characterised by a
long period of mild decline, followed by a terminal
phase of accelerated decline (Figure 1). e model
estimates are reported in Table 2.
Table 1. Characteristics of deceased nursing home residents
* M = Mean. SD = Standard deviation
Figure 1. Trajectories of functional health (reversed RAI-MDS ADL index) in nursing home residents with no dementia, mild-to-moderate
dementia, and severe dementia. The figure shows the estimated trajectories from the multiphase model. Transition points were esti-
mated -2.22 months before death for the no dementia group, -2.29 for the mild-to-moderate dementia group, and -2.19 for the severe
dementia group. Estimated pre-terminal and terminal slopes were -0.12 and -1.53 points per months (no dementia), -0.14 and -1.25
(mild-to-moderate dementia), and -0.18 and -1.25 (severe dementia). ADL = Activities of daily living.
18
GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
The transition point for the reference group
without dementia was estimated around two
months before death (y30 = -2.22). Terminal
decline (y20 = -1.53) for this group was almost 13
times (y20/y10 = 12.95) larger than pre-terminal
decline (y10 = -0.12). Both residents with mild-
to-moderate dementia and severe dementia had a
steeper pre-terminal decline (y11 = -0.03 and y12 =
-0.06), and less terminal decline (y21 = 0.27 and y22
= 0.28). However, terminal decline was still more
than 8 times ((y20 + y21)/(y10 + y11) = 8.74) larger
in the mild-to-moderate and 7 times ((y20 + y21)/
(y10 + y11) = 7.15) larger in the severe dementia
group compared to pre-terminal decline. The
transition point to the terminal phase of both
dementia groups did not differ from those without
dementia, but residents with higher functional
health showed earlier transition points to the
terminal phase (r = -.58). As expected due to the
classification of severe dementia, residents with
severe dementia had lower functional health (y03
= -4.84). Except for the intercept, estimates of the
dementia groups were similar.
DISCUSSION
Our study is the rst to quantify the nonlinear
pattern of end-of-life trajectories in functional health
of nursing home residents and to explore the eect of
dementia status and severity on rates of change and
time of transition to terminal decline. Independent
of dementia status and severity, functional health
remained relatively stable with only mild decline
for most of the time during the last two years of life,
followed by a steep decline (up to 13 times larger than
before) in the last two to three months before death.
Although residents with dementia showed steeper
decline in the pre-terminal phase and less steep
terminal decline in the last months of life, terminal
decline was still at least seven times larger than in
the pre-terminal phase. Dementia status or severity
did not signicantly aect the transition point, so
the onset of terminal decline occurred in the same
timing pattern in all groups.
Our results conrm previous ndings on end-of-life
trajectories in subjects dying from various causes
without dementia that describe pronounced and
Table 2. Multi-phase model for functional health (reversed ADL*) over time to death, including dementia status. †
* ADL = Activities of daily living. † Intercept centred at the transition point. Residents with no dementia served as the reference group.
‡ SE = Standard error. § SD = Standard deviation.
|| AIC = Akaike information criterion.
19
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
accelerated terminal decline before death (Teno et
al. 2001; Chen et al. 2007; Klijs et al. 2010; Gill et al.
2010). Our ndings also indicate for the rst time that
even residents with severe dementia show substantial
change in functional health before death. Our study
therefore somewhat contradicts two previous studies
reporting less pronounced or absent accelerated
terminal decline in people dying with severe dementia
(Chen et al. 2007; Gill et al. 2010). is may be
explained by our approach of trajectory analysis of
monthly rates of change as compared to assessments
with larger intervals (Chen et al. 2007) or using only
rough estimates of ADL function (Gill et al. 2010). In
addition, the study population of Gill et al. (Gill et al.
2010) was conned to community-dwelling residents
and the nursing home residents with severe dementia
in the study of Chen et al. (Chen et al. 2007) tended
to be older and more disabled, possibly reducing the
range for change. So, the eect of terminal decline
could have been previously underestimated for this
population. Our ndings are unique with regard to
the population of residents with mild-to-moderate
dementia and the long relatively stable phase of up to
two years prior to the acceleration of decline. Previous
studies were restricted to severe dementia and were
using shorter observation periods of maximally 12
months (Chen et al. 2007; Gill et al. 2010).
e study has several strengths and limitations.
e major strengths are the large dataset of routine
healthcare data and the use of an internationally
established assessment instrument. e RAI-MDS
oers a standardised instrument developed and
validated for the purposes of nursing home settings.
e instrument shows an adequate to excellent level
of reliability, in particular for the ADL domains
(Hawes et al. 1995; Sgadari et al. 1997; Morris et al.
1999; Poss et al. 2008). ADL scales based on the RAI-
MDS also show high internal consistency (Morris et
al. 1999) and are adequately change-sensitive even
for residents with severe dementia (Carpenter et
al. 2006). Use of ADL measures based on the RAI-
MDS is in line with World Health Organization
recommendations to measure functional
impairment and disability (Morris et al. 1999).
e wide distribution of the RAI-MDS permits
rapid replication and implementation in similar
settings, and comparison of dierent populations
or healthcare systems. Furthermore, our study is
the rst to use multi-phase growth modelling, the
most adequate statistical approach to quantify the
dierent periods of functional change before death.
e study dataset can be considered as representative
for the RAI-using nursing homes in Switzerland, at
least for the German and Italian speaking parts of
Switzerland with high coverage of the RAI system
(Anliker and Bartelt 2015).
ere are a number of limitations that need to be
addressed. While our ndings describe the typical
situation of long-term care residents, we do not know
whether they apply to non-institutionalised persons
with dementia. Persons with dementia in nursing
homes appear to dier from those dying at home
(Mitchell et al. 2004). In nursing home residents,
terminal functional decline in dementia could be
more pronounced, as the nursing homes might be
better able to stabilise functional health in the pre-
terminal phase. Future research needs to address if
our results apply to other populations or healthcare
settings by comparing dierent populations that
are assessed with RAI instruments. Although our
ndings help to understand the course of dying
by describing the prototypical scenario in which
functional health develops towards the end of life,
not every single person with dementia in a nursing
home will follow this course. e predictive utility is
limited due to the individual variability that cannot be
explained on the basis of available data. In addition,
the analytic model was based on both cross-sectional
and longitudinal data because of the data structure
with dierent individual assessment time points
and number of assessments. As a consequence, our
ndings cannot be directly implemented in the RAI-
MDS. Finally, the dataset is conned to Switzerland.
We do not know in how far variations in the design
of nursing homes, number and qualication of
sta and the culture of care may inuence terminal
trajectories (for example, emphasis of palliative care
over ‘conventional’ medical care approaches).
Our ndings indicate that nursing home residents
with and without dementia can expect to face a
long phase with only mild functional decline, in
other words relative stability in their functional
health, before death becomes immanent and their
functional health declines sharply. Our study has
broad implications for stakeholders, care practice
and research. Life in a nursing home is oen
feared and seen as a state of severe and progressive
dependence and impairment. Our study shows that
functional stabilisation is possible, even in residents
suering from severe dementia. Knowledge about
the chance for stabilisation and the relatively short
dying phase could attenuate fears with regard to
nursing home placement. Knowledge about a typical
20
GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
dying phase in a nursing home could furthermore
help to prevent unnecessary and burdensome
medical interventions. In addition, our results
have methodological implications. Poor levels of
functional health have repeatedly been reported as
one of the most important factors associated with
mortality in long-term care in both residents with
and without dementia (Abicht-Swensen and Debner
1999; Flacker and Kiely 2003; Porock et al. 2005; Lee
et al. 2009; Hjaltadóttir et al. 2011). Our ndings
imply that instruments to predict health or mortality
should consider the dynamics of trajectories rather
than absolute levels of functional health. is includes
predictive models for non-cancer patients, for which
the needs and timing for palliative care are not well
understood (Coventry et al. 2005). Previous studies
using rough estimates for change already indicated
an advantage of including functional change in tools
for mortality prediction in long-term care (Hirdes et
al. 2003; Yeh et al. 2014)10. However, since the drop
in functional health occurs only two to three months
before death, the use of such tools for prediction over
longer time periods would appear to be limited. Our
results may furthermore have implications for the
biological understanding of the dying process. From
developmental psychological research, it is known that
accelerated decline occurs in various health-related
domains, such as cognition, well-being, and subjective
health status, usually described as ‘terminal decline’ or
‘terminal drop’. e dynamics of this decline seem to
be driven by time to death, rather than age or specic
disease (Wilson et al. 2012; Gerstorf and Ram 2013).
Our study is the rst that observed terminal decline
in functional health in people dying with dementia
that is comparable to the pattern seen in people dying
without dementia. So, our results point toward the
existence of similar end-of-life health dynamics in
residents with and without dementia, which may
reect the natural process of dying.
However, while various health-related domains seem
to show terminal decline with early transition several
years before death (Wilson et al. 2012; Gerstorf and
Ram 2013), terminal decline in functional health
manifests itself as late loss that typically occurs just
months or weeks before death. Eventually, there
could be important implications for care practices
and health systems. Knowledge about the dynamics
of functional health at the end of life helps to
optimise healthcare provision in the terminal phase,
including the practice of providing palliative care for
people dying with dementia. Moreover, stabilised in
functional health or basic ADLs has been discussed
as a quality marker in long-term care (Morris et
al. 1999). It needs to be further explored whether
eective stabilised of functional health can be used
to compare dierent healthcare settings and systems
in terms of quality of care, in particular for residents
with dementia.
Future studies should try to identify predictors that
explain more of the variability in end-of-life trajectories
and better discriminate between those residents with
terminal decline in functional health and those without.
In addition, specic factors associated with immediate
functional decline that increase mortality should be
investigated. Future studies should also investigate end-
of-life trajectories in other health domains and analyse
the reciprocal eects of dierent health trajectories to
identify the directional relationship between dierent
health parameters.
CONCLUSIONS
e nursing home population has a relatively stable
functional health in the last two years of life until
they enter a phase of rapid decline two to three
months before death. is terminal decline occurs
independently of dementia status or severity,
presumably indicating disease-independent
mortality processes.
Our ndings may help to better distinguish between
dierent stages at the end of life and to better identify
the onset of the terminal phase in nursing home
residents with and without dementia. erefore,
our results improve the understanding of the dying
process and have broad implications for optimising
end-of-life care in nursing homes.
DECLARATIONS
Funding: is work was funded by grants from the
Swiss National Science Foundation (NRP 67 ‘end of
life’) and the Bangerter Rhyner Foundation (through
the Swiss Academy of Medical Sciences Grant
Program ‘Health Services Research’).
Conict of interest: all authors report no conicts
of interest.
Ethics approval (include appropriate approvals
or waivers): as the study was based on anonymous
routine care data only, the requirement for ethics
approval was waived by the cantonal ethics committee
Zurich (declaration of no objection 103-2015, KEK-
ZH-Nr. 2012-0102).
21
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
Availability of data and material: for verication
purposes, the dataset is available from the
corresponding author upon request.
Authors’ contributions: NT: data preparation and
data analysis, interpretation of data, draing the
manuscript. DG: supporting data analysis, revising
manuscript. SE: interpretation of data, revising
manuscript. HG: interpretation of data, revising
manuscript. CR: study design and conception,
revising manuscript. MM: study design and
conception, revising manuscript. HW: study design
and conception, interpretation of data, revising
manuscript FR: study design and conception,
interpretation of data, revising manuscript. All
authors contributed important intellectual content
to the manuscript. All authors approved the nal
version of the manuscript.
ACKNOWLEDGMENTS
We thank PD Dr. med. Albert Wettstein for his
valuable inputs and support. We thank Q-Sys AG, St.
Gallen, for help with data management.
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Supplementary Appendix
Formulas and equations of the multi-phase ‘spline’
models
e multi-phase or ‘spline’ growth models were
specied as:
and
e equations Eq. (A.1) and Eq. (A.2) represent the
functional health of resident i at time t as a function
of the individual-specic intercept β0i at a person-
specic change point ki, the individual-specic
slopes β1i and β2i for functional change per month
before and aer the change point ki, and the residual
error eti.
ℎℎ = 0 + 1 ( − ) + ,
ℎ ℎ < (. . )
ℎℎ = 0 + 2( − ) + ,
ℎ ℎ ≥ (. . )
23
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
For the model with dementia status as predictor,
individual-specic intercepts at change point, β0i,
change point, ti, and the two slopes, β1i and β2i, were
modelled as a function of dementia status, as follows:
e γs in equations Eq. (A.3) - Eq. (A.6) represent
sample-level associations and the us the residual
unexplained individual dierences that are assumed
to be multivariate normally distributed, correlated
with each other, and uncorrelated with the residual
error, eti. In addition, estimates for the ratios between
terminal and pre-terminal slopes were calculated for
each of the three groups. e mean levels (± SD) of
functional health (reversed activities of daily living
(ADL) index) for the last 24 months are displayed in
Table A1.
Formulas and equations of the multi-phase ‘spline’
models with covariates
e resident assessment instrument – minimum
dataset (RAI-MDS) provides a large number of
other factors potentially related to functional health.
However, to keep the statistical models as parsimonious
as possible, the selection of covariates was reduced to
demographic variables (age at death and sex) and those
variables directly linked to the functional health scale
(training and skill practice in ADLs, the residents’ belief
in future improvement of at least some ADLs, the sta’s
belief in future improvement of at least some ADLs,
daily variability in ADLs, and the possibility to perform
ADLs though only slowly). Within the RAI-MDS,
training and skill practice is documented for any of the
particular ADLs. Here, only the four basic ADLs related
to the ADL index were considered and merged to one
single variable of receiving at least one of the four ADL
care trainings or not.
To avoid time-varying predictor variables, the
independent variables were aggregated, so the
residents were classied into a specic group whenever
a characteristic appeared in at least one of the available
assessments (for example, the resident was classied
as receiving training and skill practice when training
and skill practice for one of the four basic ADLs was
documented at least once during his or her nursing
home stay). Finally, age at death was centered at the
mean level. A summary of the ADL-related items in
RAI-MDS used as covariates is displayed in Table A2.
In the full model with all covariates, individual-specic
intercepts at change point, β0i, change point, ti, and the
two slopes, β1i and β2i, were modelled as a function of
dementia status and the covariates, as follows:
0 = 00 + 01( )
+
02( )+ 0 (. . )
1 = 10 + 11 ( )
+12( ) (. . )
2 = 20 + 21( )
+
22( ) (. . )
= 30 + 31( )
+32( )+ 3 (. . )
0 = 00 + 01( )+ 02 ( )+ 03( ℎ)+ 04()
+
05
(
)+
06
(
′
)+
07
(
′
)
+ 08( )+ 09( )+ 0 (. . )
1 = 10 + 11( )+ 12( )+ 13( ℎ)+ 14()
+ 15( )+ 16(′
)+ 17(′
)
+ 18( )+ 182( ∗ )
+ 19( ) (. . )
2 = 20 + 21( )+ 22 ( )+ 23( ℎ)+ 24()
+
25
(
)+
26
(
′
)+
27
(
′
)
+ 28( )+ 29( ) (. . )
= 30 + 31( )+ 32 ( )+ 33( ℎ)+ 34()
+ 35( )+ 36(′
)+ 37(′
)
+ 38( )+ 39( )+ 3 (. . )
24
GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
0 = 00 + 01( )+ 02 ( )+ 03( ℎ)+ 04()
+ 05( )+ 06(′
)+ 07(′
)
+ 08( )+ 09( )+ 0 (. . )
1 = 10 + 11( )+ 12( )+ 13( ℎ)+ 14()
+ 15( )+ 16(′
)+ 17(′
)
+ 18( )+ 182( ∗ )
+ 19( ) (. . )
2 = 20 + 21( )+ 22 ( )+ 23( ℎ)+ 24()
+ 25( )+ 26(′
)+ 27(′
)
+ 28( )+ 29( ) (. . )
= 30 + 31( )+ 32 ( )+ 33( ℎ)+ 34()
+ 35( )+ 36(
′
)+ 37(
′
)
+ 38( )+ 39( )+ 3 (. . )
e γs in equations Eq. (A.7) - Eq. (A.10) represent
sample-level associations and the us the residual un-
explained individual dierences that are assumed
to be multivariate normally distributed, correlated
with each other, and uncorrelated with the residual
error, eti. Stepwise model estimation was performed
with variable implementation in the displayed order.
Interaction eects of the covariates with dementia
were tested for all the covariates, but only one inter-
action (γ182) was reliably dierent from zero, so the
other interaction terms were removed from the nal
model. In addition, estimates for the ratios between
terminal and pre-terminal slopes were calculated for
each of the three groups. e covariates were coded
using the weighted eects according to their distri-
butions (for frequency distribution of the covariates
see Table A2), so the eects represent deviations of
the grand mean instead of the group mean or a refer-
ence group, which is for example the case when using
dummy coding. As a consequence, the models’ pa-
rameters can be interpreted in terms of controlling
for the covariates’ eects and not under the specic
condition of the actual reference groups (i. e. always
the group that is coded with 0). Results of the full
model with covariates are displayed in Table A3.
Table A1. Mean levels (± SD) of functional health (reversed ADL* index) over time to death
25
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
* ADL = Activities of daily living. † M = Mean. ‡ SD = Standard deviation.
Figure A1. Terminal trajectories of function health (reversed RAI-MDS ADL index) of a randomly selected subsample of 300 nursing
home residents. The individual trajectories are displayed as a function of dementia status (no dementia, mild-to-moderate dementia,
severe dementia).
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GLOBAL PSYCHIATRY ARCHIVES — Vol 6 | Issue 1 | 2023
Table A2. ADL-related items in RAI-MDS used as covariates
* ADL = Activities of daily living
Table A3. Multi-phase model for functional health (reversed ADL*) over time to death, including dementia status and covariates.†
27
GLOBAL PSYCHIATRY ARCHIVES — Terminal functional decline
* ADL = Activities of daily living. † Intercept centred at the transition point. Residents with no dementia served as the reference group.
‡ SE = Standard error. § SD = Standard deviation.
|| AIC = Akaike information criterion