Crisis resolution teams and inpatient mental health care in England

Transcription

1 Crisis resolution teams and inpatient mental health care in England Gyles Glover Gerda Arts Kannan Suresh Babu Centre for Public Mental Health Durham University

2 Title: Crisis resolution teams and inpatient mental health care in England Authors: Gyles Glover Professor of Public Mental Health Gerda Arts Lecturer in Statistics Kannan Suresh Babu Senior Registrar in Psychiatry Address for correspondence: Centre for Public Mental Health University of Durham Elvet Riverside Building New Elvet Durham DH1 3JT Page 2/88

3 Contents. Executive Summary...4 Background:...4 Data sources and preparation...4 Findings...5 Discussion and conclusions...6 Introduction...8 Background...9 Efficacy and Effectiveness...12 The present study...13 Data sources and specifications...15 Hospital admission data...15 Compulsory admission data...16 Data about Crisis Resolution and Assertive Outreach Teams Data: overview, quality and cleaning...19 Provision of new teams...19 Inpatient admissions and bed days...24 Overall trends in admissions...26 Effect of new teams on admissions and bed use...35 Simple views of the effects of the new teams...35 Significance of the differences...38 The timing of apparent influences...38 Patterns for different age and gender groups...38 The extent of the difference...38 Association with initial admission rate level...38 Compulsory admissions...38 Overview of the data...38 The effect of new teams...38 Discussion...38 Plausibility of the data used The detailed findings...38 Do these associations suggest causality?...38 Are the findings generalisable?...38 Findings on compulsory admissions...38 Implications for future work...38 Conclusions...38 References...38 Annex Page 3/88

4 Executive Summary Background: This report describes the first stage of a study of the effects of the implementation of crisis resolution teams in England following the policy initiatives first set out in the National Service Framework for mental illness (Department of Health 1998). The work was based entirely on routinely collected data from Hospital Episode Statistics, Department of Health KP90 returns (describing the use of the Mental Health Act) and the annual mapping of English mental health services by the Centre for Public Mental Health at the University of Durham (Glover and Barnes, 2002, 2003, 2005). Key goals for the policy were the reduction of the numbers and length of admissions to mental hospitals and of the use of compulsion. In the work described here, we aim was to establish whether the implementation of crisis resolution teams had been associated with measurable changes of these kinds. Data sources and preparation The whole study was restricted to adults of working age (18 to 64). The unit of study adopted for the work on hospital admissions and bed usage was the 303 primary care organisations (PCTs) which between them covered England in PCT boundary assignments have been added to Hospital Episode Statistics records dating back as far as 1998/9. These data were thus relatively easily available at this level for the six years from 1998/9 to 2003/4. Compulsory admission data were only available for NHS trusts. Developing time series for trusts over the five years for which these data were available (1998/9 to 2002/3) therefore entailed a complicated exercise tracking the changes in trusts configurations over this period. This produced a data file using 2003 NHS trusts (or in five cases combined super-trusts ) as a unit of study. Service mapping data, indicating the availability and characteristics of crisis resolution and assertive outreach teams could be analysed according to either boundary set, and were available for the years 2001 to On inspection all three data sources contained obvious flaws. 19 PCT areas were omitted from analysis because their team provision status was unclear from the service mapping. Hospital Episode Statistics frequently showed single years data missing (or in one instance probably duplicated) for individual trusts. Where there was a clear trend, these defects were filled using calculations based on lines of best fit. In situations where more than one data point was missing or where the trend was unclear (41 cases), PCTs were omitted from analysis. 12 further PCTs were omitted as gender data were largely missing from their Hospital Episode Statistics records. For the main part of the study this left data covering 75% of the population of England. Page 4/88

5 Erratic variation in data on compulsory admissions could have arisen either from gaps in original data or from imperfections in the process of consolidating trust records through reorganisations. Of 84 trusts (and super trusts), 16 were dropped from the analysis as numbers of compulsory admissions fluctuated to an implausible extent. This left 81% of trusts covering 85% of the population. In all cases data cleaning was undertaken blind to its consequences for the analysis: PCTs and provider trusts were identified only by standard codes and team provision and activity data were joined only after this process was complete. Findings Hospital admissions and bed usage showed an overall declining trend throughout the six-year period for younger adults (aged 18 to 35) but a broadly steady pattern for people aged 35 to 64. For both age groups, the pattern arose from a wide spread of results for individual PCTs ranging from decreases in admissions of around to increases of around between the first and last two years periods of the six years studied. Crisis teams covered 16% of the population by 2001 rising to 45% by Assertive outreach teams covered of the population in 2001 and 91% by At each stage, roughly half of the crisis resolution teams provided a 24/7 on-call service, while 65% of assertive outreach teams worked during evenings and at weekends. Analyses were undertaken using both all reported teams (broad definition) and only those meeting these criteria (restrictive definition). For analysis of admissions and bed use, PCTs were categorised on the basis of the year in which they first provided each type of team. The impact of the two types of intervention was studied using a repeated measures analysis of variance, with the number of admissions in successive years as the dependent variable and taking the population size and AREA mental health needs index (Sutton et al 2002) as covariates. Implementation of crisis resolution teams, was associated with greater falls in admissions. This effect was more marked for PCTs implementing teams earlier, and for older adults and women. For younger people of both sexes it was only significant in analyses using restrictive team definitions; the effects for older adults were also substantially more statistically significant in these. Graphic presentations of the modelled trends in admission numbers suggested that the additional falls were timed appropriately in relation to the points at which crisis teams were introduced. For assertive outreach teams, the duration of team implementation seemed to make little difference but PCTs not introducing teams at all seemed more likely to have experienced falls in admissions. However the statistical significance of these differences was generally less than that for crisis teams and no significant association was seen in the models using restrictive team Page 5/88

6 definitions. No relevant time patterning was seen for the effect of assertive outreach teams. Broadly similar patterns were seen for trends in total occupied bed days, but significance levels were generally weaker. The extent of the effects on admissions and bed use attributable to the introduction of teams was examined by studying differences between the changes seen for PCTs introducing teams by 2001 and those not introducing teams within the period studied. For broadly defined crisis teams a 1 attributable fall in admissions was seen (95% confidence interval -2% to -18%, p=0.02). For crisis teams with 24/7 on-call there was a 23% attributable reduction in admissions (95% confidence interval -7% to -38%, p=0.008). The overall reductions in bed use observed for broadly and restrictively defined crisis teams were 4% and 12% respectively, but neither of these figures reached statistical significance. Significant attributable reductions in bed usage were only seen for older working age adults with 24/7 crisis resolution teams, (-19%, 95% confidence interval -2% to -37%, p=0.33). The data about compulsory admissions were much less satisfactory. Identifying local units with consistent geographical scope over the period studied proved difficult and the necessarily larger units of study inevitably gave much coarser resolution. Data were available for one less year unfortunately the year in which the largest number of teams was active. Simple comparative presentations showed decreases in compulsory admissions related positively to the extent of provision of either broadly or restrictively defined crisis resolution teams. However these differences were not statistically significant. Assertive outreach team provision appeared to produce no effect. A multivariate analytic strategy, similar to that used for admissions and bed days, was used to test the overall statistical significance of the impact of the new teams on compulsory admissions. In view of the larger units of analysis, the numbers of crisis resolution and assertive outreach teams and staff per 100k population available in 2001 were taken as covariates, along with the population size weighted for need. No robust and statistically significant effects were observed. Discussion and conclusions The discussion emphasised the importance of the data cleaning process to the reliability of the results, and set out to the main findings of the study. It went on, using the Bradford-Hill criteria, to argue that a strong case can be made that the introduction of crisis teams has been causally responsible for reductions in admissions. The issue of generalisability was discussed. The teams reported here may be leaders in their field, but between them, the PCTs setting up crisis teams by 2001 and during 2002, cover nearly a quarter of the population of England. The findings cannot therefore be dismissed as anecdotal. Page 6/88

7 The findings with regard to compulsory admission were less conclusive. The apparent direction of the difference would not rule out an influence of crisis teams in reducing these. The failure to find statistically significant evidence in favour of such an influence could have arisen from the much less satisfactory nature and scope of the data in this part of the study. Page 7/88

8 Introduction This report describes the first phase of a national study of the implementation of mental health crisis resolution teams in England. The overall project comprises two sections: an initial statistical analysis of existing, routinely collected data and an extensive and detailed survey of the teams currently in place, their history, coverage and the varying ways in which they operate. In addition to describing the overall impact of these new teams on in-patient mental health care, the first descriptive phase is intended to identify teams with apparently contrasting effects to prepare the ground for a case-control analysis of data collected in the second phase. Because they were introduced almost simultaneously and may also significantly impact on patterns of inpatient care, this first phase has also examined the pattern of implementation of assertive outreach teams. The report is a preliminary submission. It seeks to answer the question whether the implementation of new crisis resolution and assertive outreach teams seems to be being having an effect in reducing in-patient psychiatric admission and compulsory admissions, and if so, to what extent. It was requested to address questions arising in relation to the discussions of the Joint Committee on the Mental Health Bill. The conclusions the report can reach are inevitably preliminary. Any study of routine statistical information must pay considerable attention to imperfections in its data sources. The present study has done this as far as the time has allowed. Ideally, in addition to reviewing the data for implausible entries, it is desirable to undertake a substantial amount of data checking and correction with local sources. This work is planned for the second phase of the project, hence, at this stage it has only been possible to address these problems by correcting obvious errors and removing wholly implausible records. Page 8/88

9 Background Standard five in the National Service Framework for mental health, (Department of Health 1989) set out the principle that each service user assessed as requiring a period of care away from their home should receive care in the least restrictive environment, and as close to home, as possible. The wider discussion setting out how this should be done included crisis resolution and assertive outreach teams as well as acute day hospitals as part of its range of strategies. In March 2001 the Department of Health published its Mental Health Policy Implementation Guide (Department of Health 2001). This set out blueprints for these types of team and indicated more precisely the anticipated goals. A few months later, The NHS Plan (Secretary of State for Health, 2001) specified that 335 crisis resolution teams and 170 assertive outreach teams would be set up. In September 2002, Improvement Expansion and Reform (Department of Health 2002) pledged an additional 50 assertive outreach teams. Between them, these teams were intended to provide both types of cover to all parts of England. Crisis resolution teams were intended to provide more than simply a hospital at home service for mental illness. In the Policy Implementation Guidance, they were conceptualized a sort of penumbra around in-patient units, crisis beds and day hospitals. They would act as gatekeepers to in-patient units, providing intensive home treatment as an alternative to admission where possible. Where some type of period away from home could not be avoided they would identify the most appropriate type of residential placement, liaising between community and ward, and assisting in the development and implementation of plans for rapid discharge. The evidence base cited to support the policy rested on a series of trials of innovative local services, most run by pioneers in the field. The principal claims made for this type of approach in the literature are that admissions and bed use are reduced and patient satisfaction increased. The findings in relation to admissions and bed use in the key studies cited studies are summarized in table 1. The detailed figures provided by different authors vary, but the overall picture is of around of admission or bed days usage being averted. There are two exceptions to this. The daily living programme in Camberwell (Marks et al 1994) was not particularly effective at reducing the number of admissions though the reduction in bed days achieved was similarly dramatic. The study by Harrison et al (2001), a later study of a team implemented under the policy initiatives considered here, achieved a much lower level of reduction in admissions. A recent study based in Hertfordshire has attempted to identify patient characteristics associated with more or less success for crisis intervention teams trying to avert admission (Brimblecombe et al 2003). In an observational study of just under 300 consecutive referrals to two crisis intervention teams over a 12 month period, the only characteristics significantly associated with greater likelihood of admission were a high suicidal ideation at initial assessment and previous hospital admission. Harrison et al (2001) working in Manchester, found that their 'Home Options' Page 9/88

10 service was more likely to accept patients who were older at initial referral and female. Page 10/88

11 Table 1. Alterations in admission patterns described in research reports of crisis resolution/home treatment teams. Hoult et al (1984) Dean et al (1990) Marks et al (1994) Minghella et al (1998) 1. Reduced Mean 53.5 days to Mean 58.7 days to Mean 82 to 16 () duration of index 8.4 days (84%) 8.3 days (86%) admission for individuals 2. Reduced number of inpatient bed days in period starting with index referral. 2. Reduced bed occupancy 3. Reduction in number of admissions 18 beds to 5 beds in 3 years (72%) of study group not admitted vs 4% of control group. Full year ave 1.69 vs 0.53 adms 67.9 to 20.6 (reduction of 69.7%) Subsequent number of relapses in index year unchanged 76 days to 18 days (reduction of 76%) No difference, but many patients in the experimental group had admissions lasting only overnight Reduction of 58% 66% of study group not admitted initially. Over 6 months, admissions reduced by 53% Harrison et al(2001) 28% of admissions prevented Page 11/88

12 Assertive outreach teams were intended to provide intensive long term support to a particularly challenging group of clients characterised by resistance to follow up, often a very disorganised lifestyle, and frequent hospitalisation. They were to achieve this by providing a high staff to patient ratio with an emphasis on building relationships, maintaining contact, and offering highly flexible and tightly co-ordinated care, mostly in the community. The evidence about the likely impact of this type of service on hospital admission is more mixed. A Cochrane review (Marshall and Lockwood, 2004) compared assertive outreach to standard care, hospital based rehabilitation and case management. Assertive outreach showed a reduction in the occurrence of admission with an effect size of 0.59 in the first comparison and 0.2 in the second. This corresponds to finding that admission frequency for those managed with assertive outreach was at the 73 rd percentile of the first comparison group and the 58 th percentile of the second. However the only UK study used in the Cochrane meta-analyses did not show a difference in this respect (Audini et al 1994), neither did the larger UK700 study (UK700 group 2000). The other key alternative to hospitalisation for patients in acute crisis is the day hospital. NHS day-care facilities have been provided for many years and are certainly encouraged in current policy although without the level of pressure for implementation applied to assertive outreach and crisis resolution teams. Unfortunately, a recent survey by Briscoe et al (2004) indicated that these are a very heterogeneous group of services. While 66% of day hospitals indicated that they considered themselves as providing an alternative to admission, a more detailed analysis suggested that only about a quarter appeared to have acute treatment as the principal focus. Only a small proportion of these operated outside normal working hours. Efficacy and Effectiveness In health services research, a distinction is conventionally made between the efficacy of a treatment approach (whether in ideal circumstances it actually works) and its effectiveness (the extent to which, when introduced into routine clinical practice, its possible benefits are realized). Introduction of these new team types is a type of social system intervention; staff are asked to work in different ways, combinations and locations as opposed to offering different types of treatment. There are many reasons to hypothesise that, with interventions of this type, the gains seen by pioneering teams, operating in contexts favourable to their setting-up, are likely to exceed those achieved by the generality of teams in a national roll out (Pawson and Tilley 1997). The pioneering services described in the early research literature are literally the 'leaders'. In a national roll-out, a full range of services will be involved. Staff, while perhaps quite clinically able, are not necessarily natural innovators. They will have been employed to undertake a prescribed role, not to invent a new one and will therefore possibly not apply the same creativity in their local circumstances. They have no particular interest in demonstrating the superiority of the new approach to care. Financial resources are usually under pressure in mental health services, and individual teams are partly 16 th May 2005 Page 12/88

13 dependent on their leader to maintain their budget within the competitive environment of their managing trust. Pioneering services have two types of advantage here. First, they are generally only started up if they have a leader who is demonstrably able in this arena. Second, as novel services they are usually objects of interest and of pride for trust chairs and chief executives, commonly gaining significant public attention. This gives them access to the highest levels of decision making, a useful asset in weathering the inevitable periodic financial difficulties. The generality of crisis and assertive outreach teams set up during the national roll out will not have these advantages. The wide range of teams produced in a national roll out is likely to differ in many important respects. The contexts in which they work, staff morale, flexibility and skill levels are all like to vary. Case selection practices may vary either explicitly (in client groups specifically not accepted) or implicitly (as a consequence of the types of people resident in the catchment area, or taken from it by other local services). The recent pan-london study of assertive outreach teams, mostly set up in response to the policy initiatives outlined above, indicated considerable variation in the nature and practices of these teams (Wright et al 2003). While not making comparisons to a control group of patients treated conventionally, Priebe et al (2003) noted the frequency of admission and compulsory admission within its client group. The present study From the goals set out in the policy documents described above, it is fairly easy to identify the improvements in services which should be seen if crisis resolution teams are being successful. 1. Hospital admissions and stay lengths should fall. 2. Rates of compulsory admission should fall. 3. Admissions to hospitals distant from the patients home should fall. 4. Admissions to partial in-patient units, such as crisis beds should increase as a proportion of all stays spent away from home by patients. 5. Patient satisfaction should rise. While the research evidence is far less clear, at least as far as the English context is concerned, the policy goals set out for assertive outreach teams also include a reduction in the number and duration of hospital admissions. This study attempts to explore on the basis of an observational study whether the first two of these goals have been achieved for crisis resolution teams. It was based entirely on routinely collected data sources. Admission to hospital is recorded in Hospital Episode Statistics; the use of compulsion is recorded in two routine Department of Health information sources. The progressive setting up of crisis resolution and assertive outreach teams around England has been monitored annually since 2000 in the annual mental health service mapping. Data from all these sources were collated and analysed in detail. Much of the complexity of the study relates to the difficulty of handling data from routine sources which are inevitably imperfect. 16 th May 2005 Page 13/88

14 Ideally the study should have included consideration of the impact of the availability of acute day hospitals. While data about day hospital provision is included in the mental health service mapping, we decided not to include these in our model because of the heterogeneity of this type of service discussed above. We do not believe that the mental health service mapping can at present adequately distinguish between NHS day-care facilities which operate as support facilities for long-term patients and the probably small minority of those which provide acute crisis management. In the study design, we considered it much more likely that an impact of crisis resolution teams would be apparent in changes in admission rates than an effect of assertive outreach teams. In addition to the much more mixed research evidence about their effects on admissions, assertive outreach teams are only intended to provide care for a small minority of patients who receive inpatient care. Notwithstanding the fact that these individuals may have relatively frequent admissions, even if assertive outreach teams were strikingly and unambiguously effective in maintaining them without hospitalisation, the numerical impact on overall admission rates would probably be small. Assertive outreach teams were included in the modelling for two reasons. First, because although small they might have had some direct effect on admission numbers which should be taken into account. Second because their implementation could provide a second marker of services which are well managed and which implement new policy expeditiously. In the next section we describe the process of selection, collation and cleaning of the data in some detail. This is essential for an adequate assessment of the conclusions which are drawn from it and the subsequent section. The subsequent two sections set out the findings for admissions and bed use and for compulsory admissions respectively. In a concluding discussion we attempt to address the question of whether our findings indicate that recent changes in admission patterns can be considered to be at least in part a consequence of the introduction of these new teams. Unless otherwise specified, all statistical analyses were undertaking using the SPSS th May 2005 Page 14/88

15 Data sources and specifications. Hospital admission data Hospital admission data were drawn from the national hospital episode statistics. This database includes a record for every episode of consultant inpatient care in an NHS hospital in England. It includes details of the patient, including their age, date of birth and place of residence, and of the episode of care including the dates, the specialty of the consultant and the various relevant diagnoses. It is accessible through regional public health observatories. Tabulations were obtained of the numbers of admissions and stay lengths of working age adults by PCT of residence, provider trust of admission, broad age of patient (18 to 34 and 35 to 64) and broad diagnosis (ICD codes F2, F3, any other F category, no F category and no diagnosis), for patients admitted under the main specialty code 710 (mental illness) for each of the years 1998/9 to 2003/4. This specialty code excludes learning disabilities, child and adolescent psychiatry, forensic psychiatry, psychotherapy and old age psychiatry. For each year admissions occurring on the first of April, or involving discharges on or after the 31 st of March were excluded. This was to avoid distortion as a result of the substantial number of NHS trust reconfigurations occurring in the period. When these occur, all current patients are technically 'discharged' from the old trust at midnight on 31st March and 'admitted' to the new Trust on 1st April. For most of the study it was necessary to relate admission records to the primary care trust in which the patient concerned lived. These commissioning authorities have also undergone substantial reconfiguration over the period studied. Fortunately, the Department of Health has recently retrospectively added 2002 primary care trust mappings to all hospital episode statistics records dating back to 1998/9. 16 th May 2005 Page 15/88

16 Compulsory admission data Data about the use of compulsion are recorded in Hospital Episode Statistics. However the problems with both the scope and the quality of this data item in this source have proved sufficiently intractable over the years for the Department of Health statistics division to set up an additional, parallel data collection source, KP90. Data about compulsory admission to hospital were drawn from this. KP90 returns are made annually by NHS trusts undertaking mental health care and indicate the number of times in the year use has been made of compulsion under each of the various provisions of the Mental Health Act, and the number of patients involved. Detailed data for individual NHS trusts are published by the Department of Health on its web site. Data for the years 1998/9 to 2002/3 were available. KP90 was a much less satisfactory source of data about mental health care events than Hospital Episode Statistics. There were three reasons for this. First the data indicate only the trust in which care was provided. They contain no records about individual care events and give no direct indication of the location where the patients live. This is a serious drawback for a study attempting to relate patterns of use of compulsory admission powers to local home-based alternatives available for treating the patients concerned. A number of NHS mental health trusts provide specialist mental health facilities for a much wider catchment area than that to which they provide routine local mental health services. Forensic and medium secure care are commonly provided in this way. Most patients in these units would be compulsorily detained. Ideally it would be appropriate to exclude admissions to this type of tertiary care facility from the analysis. However KP90 data do not permit this as they provide only total data for provider trusts. We attempted to minimise the impact by restricting analysis to sections imposed under part two of the Mental Health Act (by doctors and social workers as opposed to the courts). Second, it was necessary to reorganise the data to produce local units for analysis that were consistent over the five years for which admission data were available. This was a complex task. Many trusts underwent structural reorganisations, most commonly mergers during this period. In some instances small sections appeared to split off as primary care trust provision in the last year. Hospital Episode Statistics were used to identify which trust provided the predominant amount of care for each of the 2002/3 primary care trust areas in each of the years for which compulsory admission data were available. On this basis trust data for earlier years was assigned to the trust serving the corresponding territory in the final year. In five cases trust realignments were more complex than simple mergers. Here it was necessary to develop 'super trust' records, comprising data for all the forerunner and successor trusts. The full details of trust realignments are set out in annex 1. Third, the unit of study was much larger. The 2002 configuration of NHS trusts to which the data were aligned comprised potentially 84 trusts and 16 th May 2005 Page 16/88

17 super trusts. This compares with the admission data potentially available for 303 primary care trusts. This much coarser resolution made it more likely that subtle patterns would be submerged. In view of these disadvantages we asked the statistics division to check for us that the data concerning compulsion in recent hospital episode statistics was of unusably poor. Sadly this proved correct 1. Data about Crisis Resolution and Assertive Outreach Teams. The progress of implemention of these two policy initiatives has been tracked annually in the national mental health service mapping since 2000 (Barnes et al 2001, Glover et al ). As the number of teams has grown, so has the proportion of the country covered by them. From 2001 to 2003, data collection was refined in two ways. First the areas covered by these teams was recorded at primary care trust level. Second a number of attributes indicating the teams fidelity to the model set out in the Mental Health Policy Implementation Guidelines was reported for each team, each year. Details of these fidelity criteria are set out in box 1. Box 1. Model Fidelity Criteria for teams Assertive Outreach Teams: 1. A team approach 2. Team caseload no larger than 12 users per staff member 3. Defined client group 4. Planned long-term working with individuals 5. Much of the work outside service settings 6. Evening and weekend availability for known clients hour on-call system for known clients. Crisis Resolution Teams 1. Multi-disciplinary team 2. Availability to respond 24 hours a day 7 days a week 3. Staff in frequent contact with service users, often seeing them at least once on each shift 4. Provision of intensive contact over a short period of time 5. Staff stay involved until the problem is resolved Service mapping data are collected in relation to the position in September of the year. This corresponds roughly to the midpoint of the hospital episode statistics or compulsory admission data year. 1 In the most recent year, of the 121 NHS provider trusts reporting use of the mental health act in KP90 returns, only 73 returned any hospital episode statistics record showing this. In 30 the number of compulsory admissions in hospital episode statistics was under half or more than double that in KP90; in only 11 were the figures within 1 of each other (Thatti, personal communication). 16 th May 2005 Page 17/88

18 These data are used for monitoring progress towards the government targets for assertive outreach and crisis resolution teams and are thus subject to considerable scrutiny. However the logistics of the process of collecting them are complex and the data showed a number of misclassifications and other errors particularly in the first year s records. 16 th May 2005 Page 18/88

19 Data: overview, quality and cleaning Provision of new teams. The numbers of teams reported in the mental health service mapping over the three years for which detailed data are available are set out in table 2. The pattern of the build up over the period shown is not altogether straightforward. On detailed inspection a number of teams, particularly in the early years, appeared clearly to have been misclassified and subsequently to disappear. These were omitted from the analysis. In some cases it was unclear whether teams had been misclassified, although there were grounds for suspecting this. In these cases the primary care trusts reported as being covered by them were omitted from the analysis on the grounds that their team status was uncertain. In a small number of instances teams appeared to merge. In these cases the final configuration was used for the analysis. Table 2. Crisis resolution and assertive outreach teams reported in annual service mapping Crisis resolution teams All teams reported Records excluded Merged Teams accepted With 24/7 working Assertive outreach teams All teams reported Records excluded Merged Teams accepted With evening/weekend working th May 2005 Page 19/88

20 Among the model fidelity criteria reported for the two types of team, one in each case stood out as a potentially useful distinguishing characteristic. For crisis teams this was the provision of 24/7 on call. At each stage roughly half of the crisis resolution teams reported provided this facility. The other four criteria were endorsed by over 85% of crisis teams, three comprising simply broad descriptions of working styles. For assertive outreach teams, the most telling attribute appeared to be the provision of evening and weekend working. This characterised consistently a little over 65% of assertive outreach teams. Five of the other six criteria were endorsed by over 93% of teams, all but one of these being descriptions of working styles. The seventh criterion, the provision of 24/7 on call, was reported by only about a third of assertive outreach teams. However its appropriateness to the role of this type of team is considered more contentious. A particularly important data completeness issue was whether the annual team records indicated the catchment area served. The complexity of this varied between places. The key respondents for service mapping are officers of National Service Framework Local Implementation Teams - self appointed groupings of health service commissioners and providers and local authorities with social services responsibilities. In some cases, where the local implementation team comprises a single primary care trust which serves a single, coterminous local authority, this is simple. Where more than one primary care trust or local authority are served, respondents are asked to indicate the current caseload, and the number of referrals in the preceding six months for the team, broken down by the detailed area in which the patients are normally resident. Teams frequently provide care for small numbers of individuals from primary care trusts which appear not to be part of their normal catchment area. To make sense of this for the present study, a rule was adopted that a team was considered to be providing a service to a primary care trust if its residents constituted in excess of 1 of the team caseload. In many cases teams appeared to receive referrals from a wider area than constituted their caseload. Caseload figures were considered likely to provide the more reliable measure of catchment areas. Hence teams were assigned to primary care trusts on this basis except in a small number of instances where only referral figures had been entered (7 crisis and 1 assertive outreach teams). Having assigned teams to primary care trusts it was possible to identify the year in which each first began to receive the relevant type of service. 15 primary care trusts were dropped from the analysis at this stage as the records left ambiguity about whether they had a crisis team, 3 because there was uncertainty about assertive outreach teams, and 1 where there was uncertainty about both. Maps 1 and 2 show the progress of implementation of crisis resolution and assertive outreach teams respectively. As described below a number of other primary care trusts were dropped from the analysis as a result of deficiencies in their admission data. The final data set comprised 229 primary care trusts. Their team status is set out in table th May 2005 Page 20/88

21 Map 1 Year of first reported Crisis Resolution Team. Unshaded PCTs are omitted because of data problems London Key a.by 2001 b.2002 c.2003 e.no team 16th May 2005 Page 21/88

22 Map 2 Year of first reported Assertive Outreach Team. Unshaded PCTs are omitted because of data problems London Key a.by 2001 b.2002 c.2003 e.no team 16 th May 2005 Page 22/88

23 Table 3. Numbers of primary care trusts included in the final data set, and numbers of year olds in their populations, classified by team status, using both broadly and restrictively defined, crisis and assertive outreach teams. Broad definitions: Teams: AO team Crisis resolution team By No team Total By No team Total to 64 year old population covered: Crisis resolution teams Assertive outreach teams Number % Number % By % % % % % % No team % % Total Restrictive definitions: Teams: AO Team with Evening and Weekend working No 24/7 crisis resolution team: By compliant team No team Total By No compliant team No team Total to 64 year old population covered: Crisis team coverage Assertive outreach teams Number % Number % By % % % % % % No compliant team % % No team % % Total th May 2005 Page 23/88

24 For crisis teams, the map seems to suggest that more sparsely populated areas introduced new teams later, and less completely than more densely populated areas. Table 4 explores this issue for both types of team. The general trend appears similar for both. For crisis teams there was a statistically significant association between population density and the year of new team introduction (chi squared =17.7, df = 8, p=0.024); for assertive outreach teams the association only narrowly missed the conventional threshold of statistical significance (chi squared =14.6, df = 8, p=0.067). Table 4. Timing of the introduction of new teams in relation to primary care trust population density. Year of introduction of Crisis team Population density 2000/1 2002/3 None Total Lowest density 3 (6.8%) 9 (20.5%) 32 (72.7%) 44 2 nd 5 (11.6%) 13 (30.2%) 25 (58.1%) 43 3 rd 5 (11.4%) 19 (43.2%) 20 (45.5%) 44 4 th 5 (11.6%) 8 (18.6%) 30 (69.8%) 43 Highest density 11 (25.) 14 (31.8%) 19 (43.2%) 44 Total 29 (13.3%) 63 (28.9%) 126 (57.8%) 218 Population Year of introduction of Assertive Outreach team density 2000/1 2002/3 None Total Lowest density 23 (16.1%) 13 (25.5%) 8 (33.3%) 44 2 nd 27 (18.9%) 8 (15.7%) 8 (33.3%) 43 3 rd 28 (19.6%) 10 (19.6%) 6 (25.) 44 4 th 31 (21.7%) 10 (19.6%) 2 (8.3%) 43 Highest density 34 (23.8%) 10 (19.6%) 0 (0.) 44 Total Inpatient admissions and bed days. Data were tabulated for the 303 primary care trusts by broad age group, gender and diagnosis. Redbridge and Waltham Forest primary care trusts in East London were excluded from the analysis at this stage as their boundaries were reconfigured in April 2003 making the admission data for the final year not comparable with that for preceding years. As a check on the overall quality of the admission data, successive annual totals for all admission in each PCT were scrutinised. In some cases these showed completely implausible variations between years. Several methods, of varying degrees of sophistication, were explored in the search for a satisfactory approach which would permit the greatest possible amount of apparently reasonable data to be used. The simplest approach was the introduction of a filter based on the ratio of numbers of admissions in successive years. Using this approach a record would be excluded if the ratio of the number of admissions in any year to that in the previous year was greater than n or less than 1/n. A number of values 16 th May 2005 Page 24/88

25 for n were explored, with 2.5 appearing the most satisfactory for primary care trusts. This excluded 64. However this approach seemed unnecessarily wasteful since the two commonest types of data problem were isolated very low or very high figures in otherwise apparently reasonable series. A number of trust information specialists were familiar with instances of such rogue figures. The commonest form of this was where data were completely absent for a provider trust for a year. This usually affected several primary care trusts. One type of event producing high figures was resubmission of data by a trust to rectify omissions in an earlier submission. In these cases, the database in which hospital episode statistics are collated should delete the earlier submission. This appears not always to happen. Rogue low figures may also arise from problems with allocation of records to primary care trusts through the postcode field. A clear instance of this showed in the area around Peterborough in the late 1990s, presumably related to the reissuing of postcodes for that area around the turn of the millennium. Records with evidently problematic figures for several years were inevitably unsalvageable. But where the oddities were confined to a single year in an otherwise coherent series, it seemed appropriate to attempt to identify and correct them. We explored a number of approaches to doing this which would be consistent and not susceptible to bias. A reasonably satisfactory indicator of the overall plausibility of admission records appeared to be provided by their coefficient of variation (the ratio of the standard deviation to the mean). Where the value of this figure for the six years admission figures exceeded 0.5 it appeared unlikely that a clear pattern could be established. 25 primary care trusts were excluded from the analysis on this basis. In addition to this filter, two regression based strategies were explored to identify individual rogue data points. Deleted residual analysis entails calculating lines of best fit for the data points omitting each in turn. A figure akin to Student s t-test is then calculated for the difference between the omitted point and its predicted value, using the standard error of the prediction. A threshold value of about 1.2 standard normal deviations seemed to give the best discrimination of unlikely values. However this approach quite often appeared to identify points adjacent to obvious errors, not the errors themselves. A second and simpler regression strategy entailed calculating a line of best fit for all six data points and comparing each observation to the resulting prediction for it by the same method. This appeared more successful in identifying obvious errors. For the subsequent statistical analysis it was necessary to develop substitute figures for individual data points deleted on this basis. These were supplied by the prediction from a line of best fit based on the remaining points. For each primary care trust the decision to accept or reject each year s data was taken on the basis of the figure for all age and sex groups combined. The resulting mask was then applied consistently across the age and sex subdivisions. All of these processes were undertaken using custom written 16 th May 2005 Page 25/88

26 Visual Basic functions in Microsoft Excel. Sample calculations were checked for accuracy against parallel calculation in SPSS The results of these data cleaning operations for each primary care trust s admission data were inspected manually by three independent observers, all blind to the identity of the trust or its status in respect of team provision. In each case the observers were asked to confirm the decision to include or replace each observation for each primary care trust. We agreed the general principles that the original data should be left alone unless evidently implausible, where more than a single year appeared doubtful the case should be deleted, and that where any possible choice about correction of a single year in a case could affect the direction of the admission trend, the case should be deleted. We disagreed about 44 cases, almost always where two observers did not consider deviations sufficiently marked to warrant interference. It seemed unlikely that these disagreement would produce any substantial difference. To test this, two data sets were produced: one representing the views of the principal investigator (the figures used in the rest of this report), the other representing all the alternate views. A set of parallel calculations was undertaken using the alternative data set. The results were almost identical. A further 12 primary care trusts were omitted as gender specific admission data for a number of years were unusable, presumably indicating problems reporting this field in hospital episode statistics. These various stages of preparation left the data for 229 primary care trusts, between them covering just over 75% of the population of England in the age groups studied, (22.6 million people). (There were 303 primary care trusts in total at this stage. 19 were omitted because of uncertainty about teams, 2, because of boundary changes, 41 because of irrecoverable general problems with the admission data and 12 because of gender data problems.) Data for bed use were filtered or smoothed in the same way, using the same set of decisions about each years data for each primary care trust. Overall trends in admissions Figure 1 shows a graph of the overall trend in psychiatric admission rates and per capita bed usage for all included primary care trusts combined. The overall decline of 13.3% in the rate seen for all people aged 18 to 64 conceals strikingly different patterns for younger and older within this broad age group. For people aged 18 to 34 there was a fall of 21.4% for men and 24.1% for women, men having an initially much higher rate. For people aged 35 to 64 there was no consistent trend and little difference between the genders; the last figure is 5.6% below the first for men and 4.4% for women. The trends in the number of psychiatric bed days used per 100k population were similar, although the figures for older adults showed a slight rise, roughly balancing the falls for younger people in the last three years. 16 th May 2005 Page 26/88

27 Underlying these overall trends there are wide differences in the changes seen in individual primary care trust areas. In figure 2 this is illustrated for admissions with a chart comprising one bar for each PCT area. 16 th May 2005 Page 27/88

28 Figure 1. Trend in admission and bed use rates 1998/9 to 2003/4. i. Admission rates Admissions per 100k populatio People 18 to 64 Males 18 to 34 Males 35 to 64 Females 18 to 34 Females 35 to /99 99/00 00/01 01/02 02/03 03/04 Year ii. Bed use Bed days per 100k populatio People 18 to 64 Females 18 to 64 Females 35 to 64 Males 18 to 64 Males 35 to /99 99/00 00/01 01/02 02/03 03/04 Year 16 th May 2005 Page 28/88

29 Figure 2. Change in admission numbers from the first to the last two years of the period studied (1999/2001 to 2002/4) for individual primary care trusts by broad age group. Females 18 to 34 Males 18 to Change in admissions Change in admissions Females 35 to 64 Males 35 to Change in admissions Change in admissions th May 2005 Page 29/88

30 We explored the feasibility of undertaking diagnosis specific analyses. Table 5 shows the trends in admission numbers figures for broad diagnostic categories. For people aged 18 to 34 the sustained downward trend is seen almost without exception. For people aged 35 to 64 the much more stable overall situation is apparent. Admissions for schizophrenia, schizotypal and delusional disorders (F2) show a small rise over the whole period. Those for mood (affective) disorders (F3), other psychiatric diagnoses and those not given a psychiatric diagnosis all finish below their starting point, but none of these falls is steady. In more detailed analyses, diagnosis specific figures proved very erratic. This may indicate that developments in the organisation of care have been associated with changes in the location of functions such as assessment and assignment of diagnoses with considerable resultant inconsistency in some places. As a result of this further diagnosis specific work was not undertaken. Table 5. Admissions by year, diagnosis and age group as a proportion of number in 1998/9 98/99 99/00 00/01 01/02 02/03 03/04 Age 18 to 34 F % % 91.4% 86.5% F % 85.6% 80.7% 81.4% 75.5% Other F % 85.4% 81.1% 81.3% 74.2% No F % 91.7% 89.8% 70.6% 72. Age 35 to 64 F % 98.8% 100.3% % F % 99.2% 99.6% 101.7% 91.9% Other F % 94.7% 96.1% 99.8% 95.4% No F % % 83.8% 85.7% Maps 3 to 6 show the pattern of these increases and decreases around the country. Maps 3 and 4 show changes in admission rates, maps 5 and 6 changes in rates of bed days use. There appeared to be a considerable amount of local clustering on these maps suggesting that neighbouring primary care trusts are likely to experience a similar changes in admission rate. This was confirmed by Moran s I test for spatial autocorrelation. This examines the correlation between values for neighbouring areas and compares this with similar calculations for a large number of random reassignments of the same numbers around the same map. From this the probability of the observed level of correlation can be estimated. Calculations were done using the GeoDa software package (Anselin et al 2004), and used 9999 permutations of the data for the reference distributions. The clustering is hardly surprising since in many instances, neighbouring primary care trusts will be served by the same mental health care provider. Map Moran s I p of observed value Admission rate, 18 to Admission rate 35 to Bed use rate 18 to Bed use rate 35 to th May 2005 Page 30/88