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Department of Psychiatry
Department of Public Health and Primary Care, University of Oxford, Oxford
Department of Forensic Mental Health Science, Institute of Psychiatry, King's College London
Department of Mental Health, St George's, University of London
Division of Neuroscience and Psychological Medicine, Imperial College, London, UK
Correspondence: Professor Tom Burns, Social Psychiatry, University Department of Psychiatry, Warneford Hospital, Headington, Oxford OX3 7JX, UK. Tel: +44 (0)1865 226474; fax +44 (0)1865 793101; email: tom.burns{at}psych.ox.ac.uk
Declaration of interest P.T. is editor of the British Journal of Psychiatry but had no part in the evaluation of this paper for publication.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Aims To test whether there is a discrete threshold for changes in intensive case management practice determined by case-load size.
Method `Virtual' case-load sizes were calculated for patients from their actual contacts over a 2-year period and were compared with the proportions of contacts devoted to medical and non-medical care (as a proxy for a more comprehensive service model).
Results There were 39 025 recordings for 545 patients over 2 years, with a mean rate of contacts per full-time case manager per month of 48 (range 35–60). There was no variation in the proportion of non-medical contacts when case-load sizes were over 1:20 but there was a convincing linear relationship when sizes were between 1:10 and 1:20.
Conclusions Case-load size between 1:10 and 1:20 does affect the practice of case management. However, there is no support for a paradigm shift in practice at a discrete level.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Small case-loads (e.g. 1:10, 1:12) are a feature of most current service prescriptions such as assertive outreach, crisis resolution teams and early intervention teams (Department of Health, 2001). The success of some of these service models in reducing the need for hospital care in several influential trials (Stein & Test, 1980; Hoult et al, 1983) has led to their extensive replication (Marshall & Lockwood, 1998) although not always with the same success (Thornicroft et al, 1998; Burns et al, 1999; Catty et al, 2002). Despite the absence of any evidence that very small case-load sizes themselves are closely associated with improved outcomes (as distinct from the comprehensive approach embodied in such model teams) (Wright et al, 2004), they are still strongly endorsed and precisely stipulated (Stein & Santos, 1998).
First attempts to explain the variation in outcome in these studies of ostensibly similar interventions explored the impact of varying model fidelity (McHugo et al, 1999; Fiander et al, 2003) and yielded mixed results. One criticism of the model-fidelity approach is that it focuses predominantly on structural and organisational aspects of the services and less so on day-to-day practice. Assessments of model fidelity are also generally based on self-report rather than direct measurement. The one published study using prospectively collected data (Fiander et al, 2003) did not find a strong association with improved outcome. A criticism of this prospective study, which had drawn its UK data from the UK700 study (Burns et al, 1999), is that its negative result could indicate either that there was no association between the factors examined, or simply that the levels of case-loads tested were badly chosen.
The UK700 trial was the first in this field to test the impact of varying only one feature between experimental and control conditions – in this instance a comparison of case-load sizes of 1:12–15 and 1:30–35. The trial was a large multisite randomised controlled trial of case management in psychosis and failed to find any impact of case-load size on hospitalisation or clinical outcomes. It has been proposed (Gournay & Thornicroft, 2000) that the experimental case-load sizes were too high and had they been smaller, as in the original study (Stein & Test, 1980), a positive outcome would have been found.
This issue is of fundamental importance. In the absence of major differences in hospitalisation rates, case-load size is the major cost driver in such services. However, a series of adequately powered trials using differing case-load thresholds is hardly feasible. Alternative methods of identifying a critical case-load size need to be considered, either to inform service provision or as the basis for a definitive trial.
Data collected in the UK700 trial have previously been used to explore the effects of case-load size on process of care of patients with severe psychotic illness (Burns et al, 2000), with the balance of medical to non-medical interventions as a proxy indicator for holistic care. The proportion of non-medical contacts was only increased when rates of contact were above about one per week and medical contacts comprised the majority when frequency was less than this. As with the original UK700 trial this process of care study was limited to two pre-set case-load levels.
In the current study we test for a relationship between the balance of medical and non-medical contacts and contact frequency to explore the impact of varying case-load sizes in the community care of individuals with severe mental illness.
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METHOD |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Constructing `virtual' case-loads from service data
The UK700 study collected detailed, prospective data on staff activity and
this confirmed that the two treatment arms did provide different patterns of
care despite the absence of an outcome difference
(Burns et al, 2000).
There were a total of 39 025 recordings for 545 patients over 2 years.
However, the data indicated a wide variation in the levels of activity between
individual patients within each treatment group. There were some patients
within the group with standard case management (case-load 1:30–35) who
had more frequent contact than some patients in the intensive case management
group (case-load 1:12–15). Using individual patient-level data it is
possible to derive a `virtual case-load' size for each patient by dividing
their mean contacts per month over the 2 years of follow-up by the mean
monthly contacts achieved by the average case manager.
Choice of service measure
The prospective service recording in the UK700 study included five
categories (face-to-face contacts, telephone contacts, carer contacts, failed
contacts, care coordination). The content of face-to-face contacts was
classified into 11 event types based on the focus of therapeutic activity
(housing, occupation and leisure, finance, daily living skills, criminal
justice system, carer issues, engagement, physical health, specific medical
intervention/assessment, medication, case conference). These were derived
using a modified Delphi approach to achieving consensus with clinicians
(Burns et al, 2000).
Activity rates for each category were calculated per patient per 30 days for
the 2 years of the study.
We chose face-to-face contact as the service measure to construct `virtual' case-loads. This measure was responsible for over 80% of all recorded activities and was the most consistently recorded across the sites. Face-to-face contacts were also the only service category where the focus of the event was recorded.
Calculation of case manager activity
Not all case managers were full-time and some also dedicated time to
patients not in the study. In order to calculate the `virtual' case-load it is
necessary first to decide the routine number of contacts per week or month
made by an average full-time member of staff. Information on this fundamental
aspect of community mental healthcare is surprisingly hard to obtain. Two
local surveys of contact frequency yielded levels that were considerably lower
than expected (Greenwood et al,
2000; Kent et al,
2003). Table 1
shows the recorded contacts in the two arms of the UK700 trial. There is
considerable variation in the calculated mean contacts per patient in each
30-day period (from 35 to 60), with more variation in the group with intensive
case management. In both groups there was about one missed contact for every
four to five contacts. The mean number of contacts and attempted contacts
recorded per case manager per 30 days was 49.7 (49.2 and 50.9 in the intensive
and standard case management groups respectively). We have taken 50 contacts
per 30 days as the level for a full-time case manager for our
calculations.
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Development of a proxy for change in clinical practice
In the previous study (Burns et
al, 2000) the proportion of `medical' contacts (where the
focus was either `medication' or `specific medical intervention/assessment')
to `non-medical' contacts (the focus was any of the other nine categories
listed previously). We have used the same proxy measure in this study.
Statistical analyses
To generate graphical representations patients were categorised according
to their notional allocation to intensive case or standard case management as
determined by study design. Calculated (`virtual') case-loads were categorised
by dividing consecutive values into 13 samples of equal sizes that reflected
differing case-load ranges. Proportions of patients in various categories were
compared using 
2 tests.
Correlations were assessed using Spearman's method owing to non-normality of the distributions. Stepwise linear regression was used to assess relationships between model of care, calculated case-loads and proportion of non-medical contacts. The proportion of non-medical contacts was the dependent factor, with care model (intensive or standard management) and calculated case-load entered as fixed factors. Stepwise linear regression was used to assess the affect of the calculated case-load size on primary and secondary outcomes, controlling for baseline variables (as specified in the original UK700 report) and baseline levels of the tested outcome variable.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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2=113, P<0.0001), suggesting that
patients in the two treatment groups really did receive distinctly different
services.
`Virtual' case-load size and non-medical contacts
Figure 2a–c shows
scatterplots of `virtual' case-load in relation to proportion of non-medical
contacts. Estimated case-load sizes are limited to 1:100 (because some
patients could only be contacted once or twice during the 2 years they
generate spuriously high virtual case-load sizes). Spearman's correlation
demonstrates a small but statistically significant negative relationship
between virtual case-load size and the proportion of non-medical contacts
(r=–0.138, P<0.005, two-tailed). Separate analyses
showed a significant relationship for the group with intensive case management
(r=–0.231, P<0.001) but not for the standard
management group (r=0.108, P<0.1). However, linear
regression analysis with the proportion of non-medical contacts as the
dependent variable and care model and grouped virtual case-load size as fixed
factors revealed no significant interaction term (care model x virtual
case-load size, F<1).
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Figure 3 presents the mean proportion of non-medical contacts according to `virtual' case-load size. The range of these steps is unequal as comparative numbers of results in each bin are required for analysis. Analysis by each individual case-load size (e.g. 10, 11, 12) was not possible because of empty cells. There was a steady increase in the proportion of non-medical contacts as case-load sizes fell from 1:19–21 to 1:9–11. The proportion of non-medical contacts was around 50% for case-load sizes below 9. The proportion of non-medical contacts varied in a rather irregular manner for case-load sizes between 1:22 and 1:34 and for sizes of 1:35 and above the proportion remained essentially stable.
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Case-load size and patient outcomes
The outcomes tested were the same as in the original UK700 study –
days in hospital (primary outcome) and psychiatric symptoms (Comprehensive
Psychiatric Rating Scale (CPRS; Asberg
et al, 1978); an adapted form of the Disability
Assessment Schedule (DAS; World Health
Organization, 1998)); quality of life (Lancashire Quality of Life
Profile; Oliver et al,
1997); and patients' satisfaction (Camberwell Assessment of Need;
Phelan et al, 1995)
(secondary outcomes). Analyses were adjusted for baseline levels of the
corresponding outcome variable and for other baseline variables (e.g. age,
months since onset) as in the original report
(Burns et al, 1999).
Results showed no significant relationship between `virtual' case-load size
and primary outcome. One secondary outcome, DAS score, was significantly
predicted by `virtual' case-load size (ß=–0.086,
P<0.005). Larger case-loads predicted an average decrease in
social disability.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Assertive community treatment
The insistence on an absolute threshold for case-loads reflects a
consistently expressed belief that there is a qualitative shift in practice
– that the assertive community treatment model is `all or nothing'
(Allness & Knoedler, 1998).
This insistence drew its legitimacy from the series of studies indicating that
assertive community treatment teams were routinely associated with a reduction
in bed usage (Marshall & Lockwood,
1998). However there have been important service changes in mental
healthcare in the USA over the past two decades which have involved more
actively managed in-patient care and the development of a clearer community
focus. These have led to a marked decrease in the potential for reduction in
bed usage as a consequence of assertive community treatment and few modern
studies can hope to achieve the dramatic reductions found by Stein & Test
(1980) or Rosenheck et
al (1995). Essock and
colleagues (2006) recently
failed to demonstrate a significant overall reduction in hospitalisation when
comparing assertive community treatment with standard case management in two
urban populations of American patients with mental illness complicated by
unstable housing and substance misuse. Overall, patients in both groups
improved but a relative reduction in hospitalisation was only achieved in the
urban centre with higher rates of institutionalisation, reflecting the
European experience (Burns et al,
2002).
Models of care
However, there is evidence that resource enhancement alone may fail to
change practice without an explicit change in model of care. Kent et
al (2003) found no
increase in psychosocial interventions used by community mental health teams
who had expressed a wish to do so despite the provision of substantial extra
clinical time. The impact of these findings is limited, however, by the
absence of evidence for an optimal, or critical, case-load size. It could be
argued that the teams studied by Kent et al
(2003) were so underresourced
that their enhancement only permitted adequate medical-model care to all
patients or, conversely, that they were already sufficiently resourced, the
extra clinical time was not needed and the level of non-medical care had been
clinically appropriate. This is similar to the criticism of the UK700 trial
– that both arms of the trial lay on one side of this crucial
threshold.
Main results
The contact frequencies reported in this trial are lower than many
clinicians would have expected or wished and there is a clear difference in
frequency between sites. However, there is no published evidence that they are
lower overall than frequencies in previously reported studies and there is
some evidence that they broadly reflect clinical practice in these teams
(Fiander et al,
2003). Why there is such a range of contact frequency in similarly
staffed teams is an interesting question and one for which carefully targeted
studies will be needed (Weaver et
al, 2003). It is, however, beyond the scope of this
paper.
Our results give little support for the importance of a clear-cut and crucial case-load threshold to dismiss the findings of the UK700 study. Figure 3 does not demonstrate a step-wise change in practice at any case-load size, but rather a dose–response curve between case-load sizes of 1:10 and 1:20. Thus the patients in these `virtual' case-loads appeared to receive steadily increasing non-medical (taken here to indicate comprehensive) care as the case-load fell. This would support the value of small case-loads (i.e. below 1:20) for the community care of individuals with severe psychotic illnesses. The `dose–response' character indicates how clinicians may be able to use extra contact time creatively. However, the argument for smaller case-loads must rest on what is going to be delivered in terms of treatments – there is no support for the idea that a certain case-load threshold triggers a quite different way of working.
Interpreting the results for case-loads above 1:21 or below 1:9 is difficult. Above 1:35 the curve is essentially flat and there is no identifiable influence of case-load size, with two-thirds of contacts being explicitly medical. However, these larger `virtual' case-loads reflect increased difficulties in maintaining contact with patients rather than planned clinical activity – what contact could be achieved, not what was considered appropriate. Limitations of the data and statistical methodology prevent us from further testing of case-loads below 1:9.
The range of case-load sizes between 1:21 and 1:35 contains an uncertain mixture of patients receiving intensive and standard case management and shows no simple consistent trend. It is difficult, and probably unwise, to try to draw conclusions from these results. Our scatterplots further support this interpretation that it is only with small case-loads that this shift in the balance of activity is demonstrated. The weak association found in the scatterplot for all patients is entirely accounted for by patients receiving intensive case management.
Case-load threshold
Burns et al (2000)
found no difference in the mean number of medical contacts per patient per 30
days between teams with case-load sizes of 1:12 and 1:15. The difference
between the teams was that the team with a case-load of 1:12 was using most of
their `extra' contacts for non-medical activity. Burns et al
speculated that teams might be prioritising medical contacts, that there could
be a clinically determined `ceiling' for such contacts in this patient group
and that once this level (approximating to 1 visit per 3 weeks) was reached
all further activity would be devoted to a broader range of non-medical
interventions.
Our current findings do not support such a `ceiling' effect for medical contacts. When the proportions of medical contacts at the different `virtual' case-load sizes were translated into absolute frequencies they rose steadily across the range. At case-load sizes of 36–44 a mean of 0.78 medical contacts were made per patient per 30 days; case-loads of 30–35 yielded 1.1 medical contacts, at 19–21 the frequency was 1.85 and by 9–11 it had risen to 2.6 per 30 days.
However, our findings should not be taken as a rejection of the importance of a fixed case-load. The emphasis placed on case-load size by assertive teams may be more related to the need for greater autonomy and an internal locus of control for the team than for perceived fidelity to the assertive approach. One of the attractions of working in an assertive outreach team is the guarantee of a limited case-load. Control over case-load size has been associated with less burnout in personnel compared with equivalent staff in community mental health teams where case-load sizes are bigger (Billings et al, 2003). Greater latitude in decision-making and lower job demands have also been associated with higher levels of job satisfaction and performance (Evans et al, 2006). By setting a limit to case-load size this control can be exercised unambiguously and transparently. What that limit needs to be remains, however, open to local consideration based on the clinical goals of the team and local needs and services.
Limitations
There are a number of obvious limitations to this exploratory study. We
report here analyses of data collected from a study designed to answer a
different question. The most severe limitation is that this study is built on
two artificially constructed proxies – a `virtual' case-load derived
from contact frequency and a rough measure of comprehensive care based on the
proportion of `medical' and `non-medical' activities. The problem for the
`virtual' case-loads is that they were not predetermined and reflect clinical
need. Any conclusions about causality (i.e. that small case-loads are
responsible for, rather than associated with, a more comprehensive
approach) can only be speculative.
Both of these measures are based on self-report by case managers. Although extensive verifications of contact frequency were conducted in the original study (Burns et al, 2000), no audits of activity or reliability exercises were conducted into the allocation of contacts to medical and non-medical categories other than to check that visits at which depot medication was administered were classified as medical.
Conclusions
Our study does not support a threshold effect for a case-load size which
significantly alters clinical practice but confirms that distinctions between
types of community services for this patient group (e.g. assertive community
treatment, intensive case management, `standard' case management) are more
likely to be differences of degree than of fundamentally different practices
(Catty et al, 2002).
Case-load sizes vary but generally sizes of 1:20 and below seem to be
characteristic of sustained intensive care in this patient group
(Wright et al, 2004).
Our study indicates a `dose response' within this range.
The UK700 study concluded with a request for less attention to precise definitions of care structures and more focus on the content of care (Burns et al, 1999). There has, however, been very little empirical investigation of what a smaller case-load would permit that a larger one would not. Presumably this is because it is considered self-evident – more care, higher quality care, a broader range of care. Weaver's qualitative approach to understanding the possible mechanisms of the impact of smaller case-loads on the process of care is a notable exception (Weaver et al, 2003). Our findings should alert researchers, clinicians and policy makers to the need for a careful critical approach to interpreting health service trials of complex mental health interventions. How extra resource is used is more important than how it is organised.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODRESULTSDISCUSSIONREFERENCES |
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Asberg, M., Montgomery, S. A., Perris, C., et al (1978) A comprehensive psychopathological rating scale. Acta Psychiatrica Scandinavica Supplementum, 271, 5-27.[Medline]
Attkisson, C., Cook, J., Karno, M., et al
(1992) Clinical services research. Schizophrenia
Bulletin, 18, 561
-626.
Billings, J., Johnson, S., Bebbington, P., et al,
(2003) Assertive outreach teams in London: staff experiences
and perceptions. Pan-London Assertive Outreach Study, Part 2.
British Journal of Psychiatry,
183, 139
-147.
Burns, T., Creed, F., Fahy, T., et al (1999) Intensive versus standard case management for severe psychotic illness: a randomised trial. Lancet, 353, 2185 -2189.[CrossRef][Medline]
Burns, T., Fiander, M., Kent, A., et al
(2000) Effects of case-load size on the process of care of
patients with severe psychotic illness. Report from the UK700 trial.
British Journal of Psychiatry,
177, 427
-433.
Burns, T., Catty, J., Watt, H., et al
(2002) International differences in home treatment for mental
health problems. Results of a systematic review. British Journal of
Psychiatry, 181, 375
-382.
Catty, J., Burns, T., Knapp, M., et al (2002) Home treatment for mental health problems: a systematic review. Psychological Medicine, 32, 383 -401.[CrossRef][Medline]
Department of Health (1999) Mental Health Modern Standards and Service Models. Department of Health.
Department of Health (2000) The NHS Plan: A Plan for Investment, a Plan for Reform. Department of Health.
Department of Health (2001) The Mental Health Policy Implementation Guide. Department of Health. 2001.
Department of Health (2002) Community Mental Health Teams – Mental Health Policy Implementation Guide. Department of Health.
Edwards, J., McGorry, P. D. & Pennell, K. (2000) Models of early intervention in psychosis: an analysis of service approaches. In Early intervention in Psychosis: A Guide to Concepts, Evidence and Interventions (eds M. Birchwood, D. Fowler & C. Jackson), pp. 281-314. John Wiley and Sons.
Essock, S., Mueser, K., Drake, R., et al
(2006) Comparison of ACT and standard case management for
delivering integrated treatment for co-occurring disorders.
Psychiatric Services,
57, 185
-196.
Evans, S., Huxley, P., Gately, C., et al
(2006) Mental health, burnout and job satisfaction among
mental health social workers in England and Wales. British Journal
of Psychiatry, 188, 75
-80.
Fiander, M., Burns, T., McHugo, G. J., et al
(2003) Assertive community treatment across the Atlantic:
comparison of model fidelity in the UK and USA. British Journal of
Psychiatry, 182, 248
-254.
Gournay, K. & Thornicroft, G. (2000)
Comments on the UK700 case management trial. British Journal of
Psychiatry, 177, 371
.
Greenwood, N., Chisholm, B., Burns, T., et al
(2000) Community mental health team case-loads and diagnostic
case-mix. Psychiatric Bulletin,
24, 290
-293.
Hoult, J., Reynolds, I., Charbonneau-Powis, M., et al (1983) Psychiatric hospital versus community treatment: the results of a randomised trial. Australian and New Zealand Journal of Psychiatry, 17, 160 -167.[Medline]
Kent, A., Fiander, M. & Burns, T. (2003) Does extra staff change clinical practice? A prospective study of the impact of extra resources in mental health teams. Acta Psychiatrica Scandinavica, 107, 50 -53.[CrossRef][Medline]
Marshall, M. & Lockwood, A. (1998) Assertive community treatment for people with severe mental disorders. Cochrane Database of Systematic Reviews issue 2. Update Software.
McHugo, G. J., Drake, R. E., Teague, G. B., et al
(1999) Fidelity to assertive community treatment and client
outcomes in the New Hampshire dual disorders study. Psychiatric
Services, 50, 818
-824.
National Institute for Clinical Excellence (2002) Schizophrenia. Core Interventions in the Treatment and Management of Schizophrenia in Primary and Secondary Care. NICE.
Oliver, J. P., Huxley, P. J., Priebe, S., et al (1997) Measuring the quality of life of severely mentally ill people using the Lancashire Quality of Life Profile. Social Psychiatry and Psychiatric Epidemiology, 32, 76-83.[CrossRef][Medline]
Phelan, M., Slade, M., Thornicroft, G., et al
(1995) The Camberwell Assessment of Need: the validity and
reliability of an instrument to assess the needs of people with severe mental
illness. British Journal of Psychiatry,
167, 589
-595.
Rosenheck, R., Neale, M., Leaf, P., et al
(1995) Multisite experimental cost study of intensive
psychiatric community care. Schizophrenia Bulletin,
21, 129
-140.
Smyth, M. G. & Hoult, J. (2000) The home
treatment enigma. BMJ,
320, 305
-309.
Stein, L. I. & Test, M. A. (1980) Alternative to mental hospital treatment. I. Conceptual model, treatment program, and clinical evaluation. Archives of General Psychiatry, 37, 392 -397.[Abstract]
Stein, L. I. & Santos, A. B. (1998) Assertive Community Treatment of Persons with Severe Mental Illness. W. W. Norton.
Thornicroft, G., Strathdee, G., Phelan, M., et al,
(1998) Rationale and design. PRiSM Psychosis Study I.
British Journal of Psychiatry,
173, 363
-370.
Weaver, T., Tyrer, P., Ritchie, J., et al
(2003) Assessing the value of assertive outreach. Qualitative
study of process and outcome generation in the UK700 trial. British
Journal of Psychiatry, 183, 437
-445.
World Health Organization (1998) WHO Psychiatric Disability Assessment Schedule (WHODAS) With a Guide to its Use. WHO.
Wright, C., Catty, J., Watt, H., et al (2004) A systematic review of home treatment services. Classification and sustainability. Social Psychiatry and Psychiatric Epidemiology, 39, 789 -796.[Medline]
Received for publication May 3, 2006. Revision received September 22, 2006. Accepted for publication November 1, 2006.
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  P. Harrison-Read Intensive case management: targeted population as important as case-load size The British Journal of Psychiatry, June 1, 2007; 190(6): 537 - 538. [Full Text] [PDF]
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) and intensive (
) case
management. Trendlines with moving averages of 2 were calculated and
superimposed to demonstrate overall patterns more clearly.
