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Trimbos Institute (Netherlands Institute of Mental Health and Addiction), Utrecht, and Department of Clinical Psychology, Vrije Universiteit, Amsterdam
Department of Psychiatry, Free University Medical Centre, Amsterdam
Mentrum Mental Health Care, Amsterdam
Trimbos Institute, Utrecht, and Department of Clinical Psychology, Free University, Amsterdam
Department of Psychiatry, Free University Medical Centre, Amsterdam
Trimbos Institute, Utrecht, and Department of Psychiatry, Free University Medical Centre, Amsterdam, The Netherlands
Correspondence: Dr Filip Smit, Trimbos Institute, Netherlands Institute of Mental Health and Addiction, PO Box 725, 3500 AS, Utrecht, The Netherlands, Tel: + 31 30 2959254, fax: + 31 30 2971111, email: FSmit{at}Trimbos.NL
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Aims To identify subgroups at increased risk of developing anxiety in later life.
Method Anxiety was measured with the Hospital Anxiety and Depression anxiety sub-scale in 1931 people aged 55-85 years followed over 3 years. Risk factors were identified that had a high combined attributable fraction, indicative of substantial health gains when the adverse effect of the risk factors can be contained.
Results Factors significantly associated with increased risk of developing anxiety included sub-threshold anxiety, depression, two or more chronic illnesses, poor sense of mastery, poor self-rated health and low educational level.
Conclusions The identified risk groups are small, thus providing prevention with a narrow focus, and health gains are likely to be more substantial than in groups not exposed to these risk factors. Nevertheless, more research is needed to produce evidence on target groups where prevention has optimal impacts.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Measures
Anxiety
Anxiety was measured with the anxiety sub-scale of the Hospital Anxiety and
Depression Scale (HADS; Zigmond &
Snaith, 1983). The HADS was constructed with the aim of avoiding
overlap between symptoms of anxiety, depression and physical illness. Its
anxiety sub-scale (HADS-A) consists of seven items, for example `Lately,
worrying thoughts go through my mind'. Each answer is rated on a four-point
scale, ranging from 0 (rarely or never) to 3 (mostly or always). The scale
scores range from 0 to 21, with higher scores reflecting higher anxiety
levels. The HADS-A has good psychometric properties
(Mykletun et al,
2001). The scores were dichotomised at the cut-off score of 
8
(Snaith, 2003). In this paper
a HADS-A score equal to or greater than 8 is referred to as `anxiety'.
Measurements were taken at baseline (t0) and at first
follow-up (t1). Incident cases were identified when two
criteria were met: absence of anxiety at t0 (HADS-A <8)
and presence of anxiety at t1 (HADS-A 8).
Risk indicators
It is appropriate to conduct indicated prevention, or early intervention,
in people who have some symptoms of anxiety but who do not yet meet the
diagnostic criteria of the full-blown disorder
(Mrazek & Haggerty, 1994).
Therefore, sub-threshold anxiety is a relevant risk indicator. Sub-threshold
anxiety was defined as an HADS-A score above the population mean of 3 and
below the cut-off of 8. Furthermore, it is appropriate to conduct selective
prevention in people who are at a higher risk of anxiety because they are
vulnerable and exposed to risk factors. Following the vulnerability-stress
theory (Brown & Harris,
1978) and pertinent research
(De Beurs et al, 2001;
Schoevers et al,
2003,
2005), the following risk
indicators were included.
Depressive symptoms. Depressive symptoms were ascertained with the Center for Epidemiological Studies Depression scale (CES-D; Radloff, 1977). The CES-D consists of 20 items and its total score has a range between 0 and 60. Scores of 16 or over indicate clinically significant levels of depressive symptoms (Berkman et al, 1986). At this cut-off score sensitivity is 100% and specificity is 88% for DSM-IV Axis I depressive disorder (American Psychiatric Association, 1994) in the Dutch population older than 55 years (Beekman et al, 1997). In this paper CES-D scores of 16 or over are referred to as `depression'.
Chronic illness. Chronic illness refers to the most prevalent chronic physical disorders among older people, such as diabetes mellitus, chronic obstructive pulmonary disease, cardiovascular disease, arthritis and cancer (Kriegsman et al, 1996). The chronic illness variable was dichotomised as 0 (no illness or one illness) or 1 (two or more illnesses); because the majority of older people have a least one chronic illness, dichotomising at one illness would be unlikely to have much discriminatory or predictive power. It is worth noting that the physical disorders were reviewed in detail during the interview: symptoms were checked, and it was ascertained whether the participant was receiving medical attention for that particular physical disorder. In addition, the congruence between the self-reports and the medical files of the general practitioners was checked, and found satisfactory. Moreover, concordance between self-reports and general practitioners' data did not depend on depression or anxiety status (Kriegsman et al, 1996).
Functional limitations. Functional limitations were measured with an adaptation of the Organisation for Economic Cooperation and Development (OECD) indicator for functional limitations (Van Sonsbeek, 1988); this variable was coded as 0 (none or one limitation) or 1 (two or more limitations).
Self-rated health. Answers to the question, `How do you rate your health?' were coded as 1 (poor health, sometimes good/sometimes bad, fair) or 0 (good or excellent health).
Mastery. Low mastery was measured using the abbreviated (five-item) version of the (seven-item) Pearlin Mastery Scale (Pearlin & Schooler, 1978) and dichotomised at the median (1, score below the 50th percentile on the scale; 0, score above 50th percentile).
Other variables. The following socio-demographic variables were also included in the analyses: male gender (1, female; 0, male), old age (1, older than 75 years; 0, younger), low educational level (1, elementary or less; 0, more than elementary), living in an urban environment (1, living in Amsterdam; 0, living elsewhere) and small social network (1, fewer than 13 persons; 0, 13 or more persons).
It should be noted that all risk indicators were measured at t0, thus well before the outcomes at t1, and were dichotomised prior to the analysis, such that the index category (coded 1) was the assumed higher risk compared with the reference category (coded 0).
Analysis
All analyses took into account that the data were generated by a sampling
design with intentional oversampling of the male and older age strata, and
some amount of loss to follow-up. Therefore, the data were weighted such that
the multivariate sample distribution over gender and age was exactly the same
as in the general Dutch population in the age range of 55-85 years as reported
by Statistics Netherlands
(http://www.cbs.nl).
In order to obtain correct 95% confidence intervals and probability values
under weighting, all variance-related statistics were obtained with the help
of the first-order Taylor series linearisation method as implemented in Stata
version 9.0 for Windows. Weighted numbers are reported, rounded to the nearest
integer, throughout the remainder of this paper. The subsequent analyses were
carried out in several steps.
Analysis of incidence
Incidence was calculated in the cohort of the population at risk - that is,
among those who were not categorised as HADS-A anxiety cases at baseline, and
for whom the HADS-A anxiety status was available at follow-up after 3 years
(n=1931). The incidence rate was obtained with the help of a weighted
Poisson model which was regressed on the HADS-A anxiety status at follow-up,
while taking into account that not all participants had equal follow-up
times.
Analysis of risks
The incidence rate ratio (IRR) helps to identify high-risk groups. For each
risk indicator the IRR was obtained by regressing the outcome (1, incident
case; 0, not an incident case) on the risk indicator in a weighted Poisson
regression model, while adjusting for all other variables in the risk set. The
IRR describes how much larger the incidence rate is in the exposed group
relative to the incidence rate in the unexposed group, controlling for
competing risks. Incidence rate ratio values larger than 1 signify an
increased risk and values smaller than 1 indicate a lower risk in the exposed
group.
For each of the risk indicators (or combinations thereof) exposure rates were calculated. The exposure rate gives the percentage of the population exposed to a risk indicator, or to a combination of risk indicators. Finally, the attributable fraction was calculated for risk indicators and combinations thereof. This indicates by how many percentage points the incidence of anxiety will be reduced when the adverse effect of the risk indicators is completely blocked (Miettinen, 1974; Rothman & Greenland, 1998). In other words, the attributable fraction puts an upper limit to the achievable health gain in the population when prevention is successful in containing the adverse effects of the risk indicators. A maximum likelihood estimate of attributable attributable fraction was obtained with the AFLOGIT-procedure in Stata for each of the risk profiles under a Poisson regression while adjusting for competing risks (Greenland & Drescher, 1993).
These statistics indicate the size of the group to be targeted (exposure rate), their risk (IRR) and the expected maximum number of preventable cases (attributable fraction). The last can also be used to quantify the economic benefits of avoiding the onset of new cases. Together, these indices of health gain and effort allow us to select high-risk groups for whom prevention is likely to be most cost-effective.
Identification of small, high-risk groups
Starting from the `long list' of available risk indicators (see
Table 1), a `short-list' was
compiled (see Table 2) using a
conventional back-stepping procedure in a multivariate Poisson model. Only
statistically significant risk indicators were retained in the model. There
are two reasons to take this approach. First, the number of tests (in the
subsequent analysis) increases exponentially with the number of risk
indicators, and extensive multiple testing would increase the likelihood of
committing a type I error, i.e. incorrectly assuming that some associations
are significant when in fact they are not. Second, extensive multiple testing
would soon become very time-consuming and make the method less attractive for
use.
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The short-list of competitive risk indicators was then used as a starting point for generating risk profiles. Each risk profile contains at least one risk indicator and often a combination of risk indicators. For each risk profile the corresponding IRR, exposure rate and attributable fraction values were calculated. Therefore it is also possible to identify risk profiles that are associated with the best values for the IRR, exposure rate and attributable fraction overall.
For the selection of the `best' risk profiles, we used the following criteria. First, we selected only risk profiles with an IRR of 5.00 or more - population segments with at least a five-fold risk of becoming anxiety cases. This was done for ethical reasons: we wanted to select only groups with seriously elevated risk levels. Second, we decided to target only population segments that formed 10% or less of the older population (i.e. where the exposure rate is 10% or less). This criterion was invoked in order to make future preventive interventions logistically and economically more feasible. When several risk profiles met these criteria, we opted for the risk profile associated with the highest attributable fraction value; that is, where we might expect the largest health gain. Here we need to point out that the criteria were arbitrary, and other thresholds could have been chosen; however, choosing other thresholds does not affect the principle of the methodology.
Systematic application of these criteria can be graphically depicted as
tree-like structures (Lemon et
al, 2003; see Figs
1 and
2). At the top of the tree we
place the risk indicator which has the best starting values of IRR, exposure
rate and attributable fraction. The risk indicator with the starting values is
called the `parental' node. `Child' nodes can appear below the `parental'
node; in a `child' node the `parental' risk indicator is combined with the
risk indicator of the `child' node. At the level of the `child' nodes the risk
indicators are selected such that the IRR remains equal to or above 5.00 and
the exposure rate drops below 10%. This process can be continued by adding
more nodes to a branch. At the end of a branch one finds a `terminal' node
that satisfies the pre-set criteria (IRR 5.00 and exposure rate
10%). If there is a choice among several terminal nodes, then one selects
the node associated with the highest attributable fraction value; that is,
where the health gain at population level is more substantial.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Incidence
In the cohort at risk (n=1931) the incidence rate was 1.82 new
cases per 100 person-years (95% CI 1.51-2.19). Accordingly, if we were to
follow 100 people at risk of developing anxiety over 1 year, we would be
likely to observe 1.82 new cases. The incidence rate is higher in women (2.45,
95% CI 1.97-3.05) than in men (1.12, 95% CI 0.79-1.60).
Model with all risk indicators
Table 1 shows the exposure
rate, incidence rate ratio, and the population attributable fraction for each
of the risk indicators, after adjusting for the effects of all other risks in
the model. In this multivariate model six risk indicators reached statistical
significance for their respective IRRs. These were low education,
sub-threshold anxiety, history of depression, presence of two or more chronic
illnesses, low self-rated health and below-average levels of mastery. The
attributable fraction of sub-threshold anxiety is large, and indicates that
55.9% of new cases of anxiety can be prevented when all cases of sub-threshold
anxiety can be identified and receive an adequate early intervention. It is
worth noting that all the risk indicators account for 87.9% of future anxiety
cases (`total attributable fraction' in
Table 1). We will return to
this point shortly.
Selecting a smaller set of risk indicators
In the next step we obtained a parsimonious multivariate model with fewer
risk indicators (Table 2). This
model is based on the smallest subset of statistically significant risk
indicators (at P<0.05). Five risk indicators were retained:
sub-threshold anxiety, depression, self-reported poor health, low mastery and
elementary education only. Using the five selected risk indicators, 82.8% of
future cases of clinically relevant anxiety can be identified (`total
attributable fraction' in Table
2). In the complete model with all risk indicators
(Table 1) this percentage was
only marginally higher. The implication is that the parsimonious model is
nearly as good for predictive purposes as the one that contained all available
risk indicators. It should be noted that we obtained nearly identical results
for a parsimonious model in which the indicator `poor self-rated health' was
replaced by `presence of at least two chronic illnesses', but then both
variables are highly correlated (OR=5.70; s.e.=0.67; P<0.001). For
that reason we also included `presence of at least two chronic illnesses' in
the subsequent analyses.
Selecting `optimal' risk profiles for indicated prevention
As is evident from Table 2,
there is some benefit in selecting sub-threshold anxiety as a starting point
for identifying the `best' high-risk group for prevention. This group is
certainly associated with a high risk; the drawback is that the corresponding
group is large (32% of the population of older people) and it is difficult to
see how prevention could be delivered to such a large population segment. Now
a number of risk indicators can be added to the risk profile
(Fig. 1). Adding depression
offers a good solution: the IRR is still larger than 5, but the exposure rate
has now dropped to 5.4%. Thus the combination of sub-threshold anxiety and
depression can be seen as a risk profile that meets the pre-set criteria.
Figure 1 also shows that adding
`low mastery' to `sub-threshold anxiety' is a good step in building a risk
profile, but the size of the corresponding target group is still too large,
and a third risk indicator must be added. This results in four terminal nodes,
all satisfying the pre-set criteria. Among these terminal nodes, it can be
seen that joint exposure to `sub-threshold anxiety', plus `low mastery', plus
`low self-rated health' yields the best attributable fraction value,
indicating a larger health gain at population level compared with the
alternative risk profiles.
Selecting `optimal' risk profiles for selective prevention
In the previous section we started with `sub-threshold anxiety'. This
approach corresponds to indicated prevention (early intervention) in groups
that already have some anxiety symptoms and are therefore at risk of
developing the full-blown disorder. However, sometimes it may be impossible
(or too complex) to identify sub-threshold cases for the purpose of
prevention. Then one would like to conduct `selective prevention' directed at
people without symptoms but exposed to easily recognised risk indicators, for
example risk indicators that are known to general practitioners, or can be
retrieved from patient files. Ruling out `sub-threshold anxiety' as a starting
point, the next best candidate is `antecedent depression'
(Fig. 2). The corresponding
population segment is not too large (exposure rate 6.9%), but the IRR falls
below the pre-set criteria. The remaining risk indicators can then be added to
the risk profile and the IRRs are increased to a level that meets the
criteria. Most of the risk indicators in
Fig. 2 are likely to be known
by a general practitioner, whereas `mastery' can be measured quickly with the
help of a five-item scale and `self-rated health' with only one question.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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Main findings
Our study shows that it is possible to use longitudinal epidemiological
data to select risk indicators that warrant interest from the prevention
perspective. These are risk indicators that are associated with a low exposure
rate, representing small groups, high incidence rate ratios (IRR), indicating
seriously elevated risk levels; and high population attributable fractions,
indicating substantial health gains at population level. The methodology of
identifying risk indicators for prevention is not new
(Miettinen, 1974;
Morgenstern & Bursic,
1982), but in the field of psychiatric epidemiology and prevention
research it has rarely been applied. In this study, we applied it to late-life
anxiety and came up with the following key findings.
First, the incidence of clinically relevant late-life anxiety is 1.82 new cases per 100 person-years, representing a substantial annual influx of new cases. Second, starting from a list of putative risk indicators, only a few were identified as interesting from the prevention perspective when the effects of the risk indicators were adjusted for competing risks. These are sub-threshold anxiety, depression, having a below-average sense of mastery, low self-rated health and having had only elementary education. It is worth noting that poor self-rated health and having two or more chronic illnesses are correlated variables that appear interchangeable. Third, the combined effect of being exposed to two, three or four selected risk indicators yields statistically significant and substantially interesting values on measures of potential health gain (IRR, attributable fraction) and effort (exposure rate). It is worth noting that the joint exposure to more risk indicators implies a smaller population segment. The intervention thus has a narrow focus, and the corresponding number of people who are the intended recipients of prevention becomes logistically manageable.
Economic ramifications
Once the costs of the disorder are known from a cost-of-illness study, then
it is possible to combine the indices of effect and effort with the costs into
an ante hoc cost-effectiveness analysis (Smit et al,
2004,
2006b). Here we will
make the corresponding calculations for two hypothetical preventive scenarios:
a `do nothing' scenario, and a scenario in which people are targeted for
prevention when they are depressed and have some anxiety symptoms.
In the `do nothing' scenario (without any preventive intervention) one would see 18 200 new anxiety cases per 1 million people in a given year, because the incidence rate is 1.82 new anxiety cases per 100 person-years. A study carried out in the USA conservatively estimated that the direct medical per-patient costs of anxiety disorders were equivalent to £844 in UK currency. In a source population of 1 million people, the `do nothing' scenario would thus entail a cost of £844x18 200 =£15 360 800 annually per 1 million source population. Now suppose that a preventive intervention is developed to contain the adverse effects of sub-threshold anxiety in people with depression. This intervention could be based, for example, on cognitive-behavioural therapy. To reduce intervention costs, it could be offered as self-help with minimal guidance. From Fig. 1 we now know that a completely successful intervention delivered to all people with depression and with sub-threshold anxiety (5.4% of the older population) would reduce the incidence of anxiety by 20.4%. In a hypothetical scenario in which 100% of the target group is reached and all receive a 100% effective intervention, then 3713 (20.4%) of the new cases would have been avoided. In a more realistic scenario of 60% coverage and a 30% success rate for the intervention (cf. Cuijpers et al, 2005), this would result in 3713x0.60x0.30= 688 avoided onsets. Avoiding 688 onsets would thus save £844x688=£580 700 per 1 million source population.
Clearly, the intervention would introduce costs of its own. We have calculated these as £285 per recipient of a preventive intervention of the type described above (Smit et al, 2006c). Again assuming a coverage rate of 60%, this would entail 3713x0.60x285=£635 000. The averted costs (£580 700 per 1 million people) may not completely offset the costs of a preventive intervention (£635 000 per 1 million people); nevertheless, the savings form a good starting point for cost-effective prevention of late-life anxiety. In short, we have a method at our disposal that could help to direct attention to high-risk groups in which preventive interventions are likely to become cost-effective. This is achieved at an early stage of the expensive and time-consuming cycle of development and evaluation of preventive interventions. Having said this, we need to add that ultimately the cost-effectiveness of a preventive intervention has to be established in a cost-effectiveness analysis alongside a randomised trial.
Strengths and limitations
Our findings have to be placed in the context of the strengths and
limitations of this study. Its strengths are the use of population-based data;
the prospective design, which enables the study of incidence and facilitates
aetiological inference; and the measurement of exposures, which is not biased
through post hoc rationalisation on the part of the participants
because at t0 they could not have any knowledge about
their future health status at t1. Furthermore, this study
is among the first to show how a statistical technique can be applied to
quantify potential health benefits and the effort required to generate these
health gains. It thus supplies the sort of methodology which is of importance
for setting a rational `research and development agenda' for preventive
psychiatry.
The limitations of this study consist in the not very detailed measurement of the exposures. We do not know for how long and how intensively the individuals were exposed. Moreover, the number of studied risk indicators is limited in that, for example, genetic and other biological risk indicators were not included. Another limitation is the measurement of anxiety with the HADS-A. This is not a diagnostic instrument. However, it has good psychometric properties (Mykletun et al, 2001), and it may be valuable as a screening instrument, especially because anxiety disorders in older people are not well recognised.
Conceptually, it would be useful to distinguish between risk indicators that are amenable to change, such as anxiety and depressive symptoms, and those that are not. It should be noted that some risk indicators are not modifiable, such as chronic illness. However, their adverse psychological effects might be contained. Finally, there are risk indicators that are not modifiable and that have effects that cannot be brought under control through preventive interventions (such as gender); however, these risk indicators are valuable from the perspective of identifying groups at risk - which was the principal aim of this paper.
Currently there is no empirical evidence that prevention of anxiety can be successful in older people, but there are examples of effective prevention of anxiety in younger age groups (see Feldner et al, 2004) and in unipolar depression (Cuijpers et al, 2005). In this Journal we have presented data on the effectiveness of preventing depression in adults (Willemse et al, 2004) and on its cost-effectiveness (Smit et al, 2006c). We believe that developing and testing preventive interventions of anxiety disorders across the lifespan is an important and emerging research field, and this calls for a rational research agenda for the future, based on the data that we now have (cf. Smit et al, 2006b).
This study and related studies (Smit et al, 2004, 2006b; Schoevers et al, 2006) were conducted in an attempt to answer the question of whether it is possible to reduce the incidence of common, disabling and costly mental disorders in a cost-effective way. Our answers are only tentative and are best regarded as working hypotheses about directions where efforts to develop preventive interventions and to test these interventions in empirical cost-effectiveness studies are likely to stand the best chances of becoming fruitful. In a next step these hypotheses have to be tested in randomised prevention trials and cost-effectiveness studies. As yet, we are only beginning to see how prevention can be directed to high-risk groups such that the health gains are maximised, while the efforts and costs to generate these health gains are minimised.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONACKNOWLEDGMENTSREFERENCES |
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REFERENCES |
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Received for publication February 9, 2006. Revision received October 19, 2006. Accepted for publication November 7, 2007.
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