Assessment and Treatment Units and. Other Specialist Inpatient Care for People with Learning Disabilities in the Count-Me-In surveys, 2006 to 2010

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1 Assessment and Treatment Units and - Other Specialist Inpatient Care for People with Learning Disabilities in the Count-Me-In surveys, 2006 to 2010 Gyles Glover and Valerie Olson

2 About the Authors Gyles Glover is Co-Director of the Improving Health and Lives Learning Disabilities Observatory. He is Professor of Public Mental Health at the University of Durham and Consultant in Public Health at the North East Public Health Observatory and Tees, Esk and Wear Valleys NHS Foundation Trust. Valerie Olson is a statistician at the Care Quality Commission. Acknowledgments The authors are grateful to Alan Rosenbach of the Care Quality Commission for permission to have access to the Count-Me-In census data. They are grateful to Samantha Booth, Bruce Calderwood, Eric Emerson, Angela Hassiotis, Julie Hopkins, Ashok Roy, Sue Turner, John Wilkinson and Kiriakos Xenitidis for comments on earlier drafts. Thanks also to Felicity Evison for cartography.

3 Assessment and Treatment Units and Other Specialist Inpatient Care for People with Learning Disabilities in the Count-Me-In surveys, 2006 to 2010 Gyles Glover and Valerie Olson IHAL

4 Table of Contents Foreword by Dame Jo Williams DBE... i Terminology and abbreviations... ii 1. Summary... iii 1.1. Findings... iii Numbers of patients... iii Adverse patient experiences... iv Commissioning patterns... v 1.2. Discussion... v Future monitoring... v 2. Introduction Description of the available data Patient selection Patient experience variables Place of residence Disabilities Data preparation and analysis Findings Overall profile of beds Numbers and trends of ward types Security profile Sector of provision Units providing A&T care Characteristics of patients receiving A&T care Age and gender Care sector Legal status Security levels in A&T care Duration of stay Patient experiences Simple analyses Variations between provider units Combining the factors influencing patient experiences Commissioning Completeness of recording Ward types Duration of stay Security levels The geography of placements Duration of stay and out of area placement... 58

5 5. Discussion Adequacy of data The substantive findings Patterns of care Adverse patient experiences Commissioning patterns Impressions Implications for future monitoring Conclusion References Annex 1: PCT level rates of recorded use of in-patient settings Annex 2: PCT level, proportion of beds by security level... 77

6 Foreword by Dame Jo Williams DBE Chair of the Care Quality Commission I welcome the publication of this statistical report by the Learning Disability Public Health Observatory. The data collected as part of the Count Me in Census between was published annually in 2006, 2007, 2008 and 2009 but for the first time in this report has been reviewed and analysed in its totality. Although the collection of this data was stopped in 2010 I believe that it still has relevance and should be viewed as such for those charged with the responsibility for making the positive changes needed in the system following the horrific abuses exposed at Winterbourne View. The report also provides additional support for all the efforts that the national organisations supporting people who use services and family members undertake. The information here will help them with their efforts to make sure that where in - patient services are needed these should be more local to communities in which people live and that the services must always strive to protect those in their care. I was pleased to see that the first mandate between the Government and the NHS Commissioning Board published on the 13 th November 2012 made clear and explicit reference about the Commissioning Board objective post Winterbourne View. The Board is charged with ensuring that Clinical Commissioning Groups working with the Local Authorities commission services that deliver safe and appropriate care. The mandate also makes clear that there is a presumption that services should be local and that there is a purposeful effort to substantially reduce the reliance on in - patient care for people with learning disabilities and autism. We now have good evidence about how people with learning disabilities and autism should be cared for and how their families must be involved and engaged in that process. The case for excellent local community based provision close to home is compelling and although there is always going to be a need for in-patient services these should be available as part of the overall system provision and not the system provision. This report provides a comprehensive analysis about commissioning patterns and behaviours and provider performance across England and when viewed alongside the Care Quality Commission national overview report of inspections of 150 learning disability services provides a powerful platform for initiating the necessary and positive changes needed to improve the experience of people with a learning disability and their families as well. Dame Jo Williams DBE Chair of the Care Quality Commission November 2012 i

7 Terminology and abbreviations In this report the term Learning Disability is used to denote the group of conditions now more commonly referred to in international literature as Intellectual Disability. The choice of term reflects common current usage in English health and social care services, in key recent documents from the English Department of Health, and Care Quality Commission and in English legislation. Glossary of Abbreviations A&T Assessment and Treatment DH Department of Health The government department responsible for health in England. LDPHO Learning Disabilities Public Health Observatory NHS National Health Service PCT Primary Care Trust QOF Quality and Outcomes Framework ii

8 1. Summary Following the identification of serious abuses of patients in Winterbourne View, an Assessment and Treatment Unit for people with learning disabilities who present behaviour that challenges, we undertook an analysis of the extent of this type of provision in England in the period from 2006 to We used data from the Count-Me-In Censuses of all inpatient provision for people with mental illness or learning disability (NHS or independent) undertaken initially by the Mental Health Act Commission and latterly by the Care Quality Commission Findings Numbers of patients Overall the number of patients reported in all in-patient beds for people with learning disability fell by 21% from 4,609 in 2006 to 3,642 in Assessment and Treatment (A&T) units made up roughly 40% of this total, and numbers appeared to remain broadly stable over the period, at least in the independent sector. NHS trends were less clear owing to erratic ward-type classification. About 60% of A&T care was provided by NHS Trusts and 40% by the independent sector. Amongst independent sector A&T providers there seemed to be a trend in the direction of a larger number of smaller sized units. This was not clear in the NHS sector. Between 2007 and 2010, the overall number of in-patients in non-secure (general) wards fell by 20% from 2,443 to 1,949. The number in low secure beds rose by 1%, from 1,238 to 1,247, and the number in medium secure beds fell by 5% from 418 to 399. The number in high secure beds fell by 13% from 54 to 47. The extent to which independent sector organisations were providing the care varied with security level. Independent sector providers were looking after 22% of the patients in non-secure A&T units, 50% of those in low-secure A&T units and 60% of those in medium secure units A&T units. Overall, 42% of all patients were detained under the Mental Health Act, 61% of those in A&T units and 31% of those in other types of ward. Three in five of those detained under the Mental Health Act, were under civil orders (imposed by medical and social work staff), two in five were under criminal orders (imposed by courts or on prisoners). Higher proportions of those in independent sector A&T units were detained (74%) than in NHS A&T units (53%). Across all ward types and specifically in A&T wards, the proportion of patients with informal legal status fell over the four years, whilst numbers detained under the Mental Health Act, rose. The greatest rise was seen in patients detained under criminal orders. Duration of stay up to the time of census was significantly longer for patients in both low and medium secure A&T wards than for those in non-secure. For non-secure wards, it was iii

9 considerably greater in independent sector than in NHS units. However for medium secure units, a smaller proportion of patients in independent sector than in NHS placements had stayed more than a year. In the most recent census, 55% of patients had been in hospital a year or more, and 38% two or more years Adverse patient experiences Five types of adverse patient experience were reported. In A&T units, in the three months prior to census: 7% of patients experienced seclusion - this appeared to be restricted to a minority of providers; 22% experienced at least one accident; 35% suffered an assault; 41% were subject to hands-on restraint; and 27% self-harmed. For a minority of patients, these were common occurrences. For example, in the previous three months in A&T units: 6% of patients suffered 10 or more assaults; 10% were subject to 10 or more episodes of hands-on restraint. At increasing levels of security, seclusion was more common, and accidents, assaults and self-harm less common. In simple comparisons, not allowing for possible differences between the groups of patients concerned, seclusion, assaults, restraint and self-harm were all more common in independent sector hospitals, and accidents marginally more common in NHS ones. When hospitals at the three levels of security were compared separately, these differences became sharper, with differences almost always favouring NHS units. Multivariate analysis indicated that after allowing for all patient and contextual factors for which data were available, sector of provision was not associated with significant differences in the risk of seclusion or accidents, but independent sector patients had a 33% greater chance of suffering at least one assault and a 61% greater chance of experiencing hands on restraint. Using this approach to modelling, roughly half of providers were associated with specifically increased or decreased likelihood that patients would experience seclusion, restraint, accidents or assaults. More were associated with increased than decreased risks. A small number of providers were associated with increases or decreases in two or, in one case, three of these types of risk. iv

10 Commissioning patterns The identity of commissioners was less completely recorded than other data. Data are presented for 2010, the period for which they were most complete. Rates of commissioning of all types of care varied widely between Strategic Health Authorities, as did durations of stay and the security profile of patients. Strategic Health Authority-level rates for all inpatients had a threefold range (from 7.5 to 24.0 patients per 1000 people with learning disability). Rates for in-patients in A&T beds had a fourfold range (from 2.7 to 11.1). The proportion of in-patients in some type of secure accommodation ranged for all patients from 14% to 78%, and for A&T patients from 29% to 88%. For the 80% of patients where this could be determined, 80% of all patients and 73% of A&T patients were in placements in their home Strategic Health Authority. However this varied widely around the country Discussion This detailed review of the Census data relating to people with learning disabilities in inpatient A&T care raised many issues. Three are drawn out. First the large range of variability between commissioners. This suggests there is no widely accepted, and evidence-based consensus about how people with learning disabilities whose behaviour challenges should be looked after. Given the importance for the lives of many vulnerable people and the large amount of public money involved this is surprising. Second, from the data on adverse patient experiences, the substantial number of individuals suffering frequent assaults, (10 or more in three months), stands out. It is difficult to avoid the conclusion that these individuals are in placements which are unable to keep them safe. Third, the number of patients on criminal orders under the Mental Health Act has shown a notable rise. This may be a result only of a greater tendency to pursue criminal proceedings where appropriate regardless of intellectual disability, or to divert people who have committed crimes from the criminal justice system into hospitals. However an alternative explanation could be that deficits in care outside hospitals are more often allowing people at risk to get into situations in which they commit serious crimes Future monitoring There is no currently satisfactory NHS dataset documenting the care of patients in A&T services. The paper finishes by outlining the structure, scope and developmental approaches which could remedy this. A dataset is needed which focuses primarily on the patients concerned, collates inputs from all relevant health and social care providers, and includes standardised assessments, details of adverse experiences and wider legal issues as well as more conventional administrative data. Development of the coding approaches to assessments will take time, as broad cross-disciplinary professional consensus will be v

11 essential to achieve wide implementation. However agreement regarding the framework and the coding for administrative elements could be achieved more quickly and should be piloted on a rapid timescale. Information system providers involved in the development of the IAPT dataset offer a useful model. vi

12 2. Introduction The Learning Disabilities Public Health Observatory (LDPHO: is one of a small number of specialist public health observatories for England. It was established by the Department of Health in April 2010 in response to a recommendation made by the Michael Inquiry into access to health care for people with learning disabilities. 1 The LDPHO is a partnership between the North East Public Health Observatory (the contract holder), the Centre for Disability Research at Lancaster University and the National Development Team for Inclusion. This report describes one of the two studies undertaken by the LDPHO in response to the reports in a BBC Panorama programme, broadcast on 21s May 2011, of ill treatment of patients in an Assessment and Treatment (A&T) Unit, Winterbourne View. Early in the exploration of the task of establishing systems to identify and guard against similar events elsewhere, it emerged that there was no satisfactory national data source documenting the extent, nature and purpose of in-patient care provided for people with a learning disability primarily because of mental and behavioural problems. Where care is provided in NHS hospitals, outline details are available from the national Hospital Episode Statistics collected and available through the NHS Information Centre. Arguably, the scope of these data is inadequate in view of the long duration and high cost of much of the care involved. However, where admissions happen in independent sector hospitals, funded by NHS commissioners, they are not included even in these national datasets. The one potential source of comprehensive information in this area was an annual census of all hospital beds for people with mental illness or learning disability undertaken in the five years from 2006 to This census, called Count-Me-In, was intended primarily to document possible differences in care provision to members of minority ethnic groups. All of the data were collected before the work on the Panorama programme began. They are thus completely independent of bias arising from perspectives or interventions arising from the programme. The census was initially set up and run jointly by the Commission for Healthcare Audit and Inspection and the Mental Health Act Commission. Following the administrative reorganisation of 2009, it was continued by the Care Quality Commission, which also holds the archive data. The LDPHO approached the Care Quality Commission and asked for access to this source to study the wider context of this issue. Initially we aimed to address five questions: How many patients from each PCT are placed in bed-based treatment units of the types covered? How does the profile of security level used vary between PCTs? 1

13 What is the pattern of challenging behaviour (including self-harm) shown by patients from different PCTs? What is the profile of placements (high or low use of seclusion, restraint and other characteristics) used by PCTs? and How does the pattern of distance (both average, and proportions at under 20 miles, 20 to 100 miles, and over 100 miles) of placements from the PCT vary? Preliminary exploration showed that a number of prior questions about the completeness of the available archive data needed to be explored first. A wider understanding of the overall pattern of care documented in the data source was also required before it was possible to make sense of the specific patterns we had initially intended to explore. We therefore widened the analysis to examine also: Whether, and to what extent the datasets were sufficiently clear for further analysis; What they indicate about the overall pattern of in-patient care provided for people with learning disabilities in the period covered with respect to types of provision, durations of stay, reasons for admission, nature of care, progress and care planning; What the information collected could indicate about the quality of care provided by individual providers, and independent- as opposed to NHS providers, and whether questions of a similar scope could potentially act as an early warning system; The extent to which trends are apparent in any of these areas. While the analysis for the study was in progress, the findings of the Care Quality Commission s programme of inspections which followed the exposure of the practices at Winterbourne View became available. 2 3 These indicated that the extent of compliance with established standards of practice for the care and welfare of people who use services and for safeguarding people who use service from abuse was generally poor, and that there were substantial differences between NHS and Independent sector hospitals in the extent of compliance. This led us to pay closer attention to the question of whether this independent data source could provide corroborative evidence of a difference in the quality of care between the two sectors of provision. 2

14 3. Description of the available data The Intelligence section of the Care Quality Commission provided access to the data files. Whilst in most respects main data tables for the five years were intact, in a number of cases contemporary metadata, notably coding lists for providers and commissioners, had not survived and needed to be reconstructed. Files comprised a main data table, including a single row for each patient in hospital on March 31 st of the year concerned. In some cases a number of accessory coding tables were also available Patient selection The census covered both mental health and learning disability in-patient care. For the purpose of reporting, patients were categorised first on the basis of the type of service they were in. Wards were described as predominantly a Mental Health or Learning Disability ward. For compatibility with published reports, we followed this convention, selecting (for most purposes) only patients in Learning Disability wards. 4-8 Most files did not include patient ages. Years, but not dates of birth were available, thus ages assigned to patients are approximate, but should not be out by more than one year Patient experience variables Five variables recorded the frequency of important patient experiences: episodes of seclusion, self-harm, accidents, assaults and hands-on restraint. The question asked was how many times these had been experienced by each patient since admission, or in the three months prior to each census day (whichever was the shorter). Actual numbers were recorded in the 2006 survey. In later data files only the ordered categorical groupings of these items used in the contemporary publications were stored (0 occurrences, 1, 2 to 4, 5 to 9 and 10 or more) Place of residence The original data collection included the home postcodes of patients normal residence. These would have provided clarity about their PCT of origin and would also have allowed detailed analysis of distance or travel time from their home to their current placement. However in the archiving process, these postcodes were truncated, presumably for security reasons. This was done by removal of the second part of the postcode, presumably in the widespread (though erroneous) belief that this would still allow mapping to local authority and other useful administrative geographies. Unfortunately in a substantial proportion of cases it does not, so we were unable to use the postcode data. Our only guide to patients place of origin was therefore the identified commissioner, a field of variable usefulness. 3

15 Disabilities A number of additional fields recorded the presence or absence (and in some cases the degree) of five types of disability: visual, hearing, autism, mobility and learning disabilities. This allowed us to identify a wider group of patients with learning disability being cared for on mental health wards, about one third the size of the group of patients on wards designated as predominantly for learning disabilities Data preparation and analysis For analysis, extracts were drawn of all the usable fields from all five data files. Consistently coded and formatted extracts of relevant and usable fields were combined into a single master table for analysis with an additional field indicating the census-year. Data processing was undertaken using Microsoft Access and Excel. Confidence intervals were calculated for rates using the Byar method and for proportions using Wilson s method as recommended in the Association of Public Health Observatories technical briefing no 3. 9 Log rank and centile calculations were done using STATA v10.1. Multivariate statistical modelling was done by V.O. at the Care Quality Commission using SPSS v19.0 and R v

16 4. Findings 4.1. Overall profile of beds This first section of findings sets out in detail the pattern of bed use apparent in the available census figures. It is somewhat lengthy because it seemed important to provide detail about the quality of the surviving data from the censuses, and to try to sketch out the overall picture of in-patient care provided for this period Numbers and trends of ward types Table 1 and Figure 1 show the overall numbers of in-patients in each of the five successive censuses by ward type. Ward type data were not available for the 2008 census. Overall, two sharp downward steps of roughly 10% were seen in total patient numbers. The first was from 2006 to This may possibly be unreliable as 2006 was the base year. The second was from 2008 to At a more detailed level, there were some quite large alterations in the make-up of this bed compliment. There was what looks like a re-classification of a number of Long-Stay to A&T beds between the 2006 and 2007 censuses and an almost matching transfer in the other direction between the last two. Table 1. Total current in patients and breakdown by ward type all years Ward type A&T 27% (1246) 37% (1553) 42% (1569) 37% (1344) 2. Long-stay (more than a year) 35% (1631) 28% (1147) 24% (878) 29% (1061) 3. Campus NHS retained beds 10% (480) 15% (603) 13% (498) 13% (466) 4. Rehabilitation 3% (156) 5% (187) 10% (357) 10% (362) 5. Short stay (less than a year) 7% (305) 4% (176) 2% (69) 2% (85) 6. Respite 4% (190) 3% (140) 3% (118) 4% (132) 7. Other 13% (601) 8% (347) 100% (4107) 6% (206) 5% (192) Total Year on year change in total -10% -1% -10% -1% A feature of this period was the programme to close NHS campus accommodation (long term accommodation commissioned and owned by the NHS, but including informal patients on A&T wards for more than 18 months without a treatment programme). This was announced in January 2006 in the white paper Our Health Our Care Our Say which estimated the number of such patients at close to In March 2009, the health minister Lord Darzi announced that the number of individuals in this type of accommodation in August 2007 had been about 2100 and that by October 2008 it was about Whilst the number of beds was monitored twice-yearly by the NHS Information Centre, bed numbers were not routinely published. By March 2010, Department of Health officials estimate that the number still in NHS campus accommodation was 806. Thus the Campus NHS retained beds figures seen here are surprising. In the early years numbers appear too low, 5

17 In-patients suggesting these beds may have been overlooked or misclassified. The final number is also lower than known numbers. However if misclassification is the explanation, it is surprising the overall number of beds did not show a greater fall. All other things being equal, we would expect to have seen an overall fall of 2,200 between the point referred to in the White Paper and the 2010 census, and a fall of 1100 between the 2007 and 2009 censuses. The overall fall seen between the March 2006 and March 2010 census points was just under However resettlement from long stay hospitals had been a policy goal for several years prior to Our Health Our Care Our Say, so it is quite likely that significant numbers of patients were resettled in the period between the time the close to 3000 estimate was made (this was undated but presumably significantly predated its publication in January 2006) and the March 2006 census point. Figure 1. Trends in in-patients by ward type Assessment and treatment 2. Long stay (more than a year) 3. Campus NHS retained beds 4. Rehabilitation Year 5. Short stay (less than a year) 6. Respite 7. Other A close inspection of the data at the level of organisations submitting it, suggested that ward type designations might have been erratic in a substantial minority of cases. 186 organisations submitted some data. As a simple test of how many units appeared significantly erratic in classifying beds, we counted the number submitting data in at least four years, with a minimum of ten patients in each year, who reported all beds as A&T in at least one year and none in at least one other. Nine providers (5%), responsible for an average of 402 beds (10%) met this test. Simple inspection, however suggested that the degree of erratic classification was substantially greater than this. Likely alternative classifications included Long-stay (the most common), Rehabilitation and Short stay. In a few cases, units seemed to have simply missed reporting for a year. The implications of these observations are that conclusions about trends in overall numbers are likely to be more reliable than trends in numbers in specific ward types. Conclusions about patients, processes and experiences in selected ward types (for our purposes mainly A&T wards), whilst valid in terms of the groups of patients described, can only confidently be said to represent what providers described as that type of ward at that point in time. 6

18 Total in-patients, all ward types Similarly, it is unlikely that reliable conclusions about trends in NHS campus beds can be drawn, as it appears that in many cases these beds were either omitted from reporting or classified in other ways, included here as long stay or other Security profile Four levels of ward security were identified in the census data: general (non-secure), low-, medium- and high secure. High secure beds were confined to the Special hospitals and are discussed very little in this paper. They are shown here for completeness. Numerically they made up just over 1% of the total beds. Ward security level data for 2006 and 2008 had not survived. Figure 2 shows the numbers of beds by ward security. The overall fall of 12% in bed numbers from 2007 to 2012 included falls of 20% in general beds and 5% in medium secure but an increase of 1% in low secure. Figure 2. Total beds, all ward types, by security level Unclear High Medium Low General Year Security level was not available for 2006 and 2008 data. Numbers represent number of placements. It is less easy to assess the stability of reporting of unit security levels as there was one fewer year of data to establish clear patterns. 7

19 Figure 3 shows the profile of security applied to patients in each of the ward types for the three years combined for which this is possible. 42% of patients in A&T units were in open (general security) wards. This compared with 58% of patients on long-stay wards, 91% in NHS Campus beds, and 70% on short stay wards. Figure 3. Security profile of in-patient placements by ward type, 2007, 2009 and 2010 combined. Assessment and treatment Long stay (more than a year) Campus NHS retained beds Rehabilitation Short stay (less than a year) Respite General Low Medium High Other 0% 20% 40% 60% 80% 100% Proportion of patients Sector of provision For three types of ward, A&T, Long-stay and Rehabilitation, the independent sector provided substantial numbers of beds. Independent sector beds made up 20% of the total in 2006, rising to 33% in Independent bed numbers increased by 244 (26%) over the whole period, while NHS numbers fell by 1211 (-33%). This section describes the overall picture. Ward-type trends are confused by the erratic classification described above. Figure 4 and Table 2 show the detail. For A&T beds there was little apparent overall trend. Independent sector patient numbers remained broadly level. NHS numbers probably also remained much the same after allowing for variations in classification. For long-stay beds, the number and proportion of patients cared for in the independent sector both rose. The number of NHS beds described as long-stay fell, though some of this may be attributable to re-classification of other ward types. The number of beds described as rehabilitation rose in parallel in both provider sectors. 8

20 Figure 4. Chart showing numbers of patients by ward type (when available) and sector of provision, all five census years Other / unspecified Respite Short Stay Rehabilitation Campus NHS retained beds Long Stay A&T Independent sector NHS Table 2. Numbers of patients by ward type (when available) and sector of provision, all five census years Independent A&T 604 (48.5%) 581 (37.4%) (38.2%) 579 (43.1%) Long-stay 210 (12.9%) 272 (23.7%) (22.0%) 343 (32.3%) Rehabilitation 85 (54.5%) 41 (21.9%) (44.8%) 195 (53.9%) Other / unspecified 41 (6.8%) 42 (12.1%) 1,091 (26.6%) 117 (56.8%) 67 (34.9%) Total Independent 940 (20.4%) 936 (22.5%) 1,091 (26.6%) 1,069 (28.9%) 1,184 (32.5%) NHS A&T 642 (51.5%) 972 (62.6%) (61.8%) 765 (56.9%) Long-stay 1,421 (87.1%) 875 (76.3%) (78.0%) 718 (67.7%) Campus NHS retained 480 (100.0%) 603 (100.0%) (100.0%) 466 (100.0%) Rehabilitation 71 (45.5%) 146 (78.1%) (55.2%) 167 (46.1%) Short stay 305 (100.0%) 176 (100.0%) - 69 (100.0%) 85 (100.0%) Respite 190 (100.0%) 140 (100.0%) (100.0%) 132 (100.0%) Other / unspecified 560 (93.2%) 305 (87.9%) 3,016 (73.4%) 89 (43.2%) 125 (65.1%) Total NHS 3,669 (79.6%) 3,217 (77.5%) 3,016 (73.4%) 2,626 (71.1%) 2,458 (67.5%) 9

21 Figure 5 and Table 3 show a similar presentation of trends in numbers of beds by security level. The breakdown is available for one fewer year. The number of patients in non-secure independent sector beds rose by 82% while the corresponding NHS number fell by 29%. The independent sector contribution went from 8% to 19% of the total. Numbers of patients in low secure independent sector wards fell from 2007 to 2009 and then rose to a 2010 figure 15% above the starting point. NHS low secure patient numbers fell by 10%. Numbers in independent sector medium secure facilities overall remained fairly constant, although the proportion rose as NHS medium secure numbers fell by 13%. Figure 5. Chart showing numbers of patients by security level (when available) and sector of provision, all five census years Unclear 2500 High Medium Low 500 General Independent sector NHS Table 3. Total numbers of patients by security level (when available) and sector of provision, all five census years. Security level Independent General 199 (8%) 391 (18%) 363 (19%) Low 514 (42%) 453 (41%) 592 (47%) Medium 223 (53%) 225 (56%) 229 (57%) High Unclear 940 (20%) 1,091 (27%) Total Independent 940 (20%) 936 (23%) 1,091 (27%) 1,069 (29%) 1,184 (33%) NHS General 2,244 (92%) 1,767 (82%) 1,586 (81%) Low 724 (58%) 640 (59%) 655 (53%) Medium 195 (47%) 177 (44%) 170 (43%) High 54 (100%) 42 (100%) 47 (100%) Unclear 3,669 (80%) 3,016 (73%) Total NHS 3,669 (80%) 3,217 (77%) 3,016 (73%) 2,626 (71%) 2,458 (67%) Percentages show the proportion of each type of patient in the respective provision sector. 10

22 4.2. Units providing A&T care The principal focus of this report is on the group of 1,250 to 1,570 patients in A&T units. Table 4 shows the number and size of units reported to be providing this type of care over the period. Overall, there were around 80 units in most years, with an average size ranging from 16.2 to 21.6 beds. However, this represented a rising number and decreasing size of independent sector units and some fluctuation in the size of NHS units with a small decrease in number, partly accounted for by mergers. The apparent variation in ward designations in some provider organisations has been noted above. The most obvious examples here were two NHS units, each with 100 or more A&T beds in 2007 and One reported a similar number as long-stay beds in 2006 and 2010, the other as short stay beds in The charts on the following page (Figure 6) show the overall profile of units by bed numbers in each of the years as reported. The number of units identified is a little less than twice the number in the survey reported in 2007 by McKenzie Davies and Mansell, and the average size of units was in almost all cases larger than the 13 places they found. 12 A few NHS units appeared to be particularly large, though it is possible that whilst managed by a single provider organisation, some of these may have been split between several sites. Table 4. Units providing A&T beds by year and sector Overall Number of units Total in-patients Average Median (inter quartile range) 10 (7 to 18) 12 (6 to 26) 11 (6 to 23) 12 (7 to 20) Range 1 to to to to 137 Independent sector Number of units Total in-patients Average Median (inter quartile range) 16 (10 to 29) 19 (10 to 31) 14 (8 to 28) 14 (8 to 23) Range 3 to 59 3 to 59 1 to 54 5 to 43 NHS Number of units Total in-patients Average Median (inter quartile range) 9 (6 to 13) 11 (6 to 17) 9 (5 to 16) 11 (7 to 17) Range 1 to to to to

23 Patients Patients Patients Patients Patients Patients Patients Patients Figure 6. Distribution of sizes of units providing A&T in-patient care. Charts show number of A&T in patients for each unit. Note different scale for Independent and NHS units Independent sector units NHS units units, 604 patients units, 642 patients units, 581 patients units, 972 patients units, 599 patients units, 970 patients units, 579 patients units, 765 patients 12

24 Age group 4.3. Characteristics of patients receiving A&T care This section describes the similarities and differences apparent between the patient groups specifically in A&T wards. Except where otherwise stated, data have been aggregated across all four censuses for which A&T beds could be identified Age and gender On average 71% of patients were male and just under half were younger than 35. 7% were aged under 20 and 16% were 50 or older. Nine patients had no useably recorded gender and three no usable age. Figure 7 shows a population pyramid. Figure 7. Population pyramid showing average number of A&T patients in four censuses by age group and sex. 65 plus 60 to to to to to to to to to to to 14 Female Male Average number of patients per census 13

25 Average number of patients At higher levels of security a higher proportion of A&T patients were male (general wards, 65%, low secure 72%, medium secure 84%, high secure 88%; chi square = 106, df=3, p<0.0001). The age profile of patients in medium secure facilities was younger than that of either general or low secure patients. The profile for low secure patients was most concentrated in the 25 to 44 age band; in general wards there were more patients at both older and younger ages. The age distributions of both low and medium secure patients differed significantly from patients in general ward settings (low secure: combined Kolmogorov-Smirnov D= , p<0.0001; medium secure: D=0.2150, p<0.0001). Figure 8 illustrates the profiles. Figure 8. Age profile of patients in A&T units by security level of ward placement, censuses of 2007, 2009 and 2010 combined. The shading of each bar indicates numbers in NHS and independent sector placements Ind Medium NHS Medium Ind Low NHS Low Ind General NHS General 0 General wards Low secure Medium secure 14

26 Average number of patients Care sector Overall 60% of A&T patients were looked after in the NHS and 40% in the independent sector. A slightly but significantly higher proportion of male A&T patients were in independent sector placements (42% of males vs. 34% of females; chi square = 22.8 df=1, p<.0001). Figure 9 illustrates the age profiles of the patients in the two sectors. This pattern is complex with the proportion of independent sector patients peaking at 45% in the age group and declining at both older and younger ages (chi square = 93, df = 6, p<0.0001). Figure 9. Age profile of all A&T patients by sector Independent NHS to to to to to to plus Age group However, Figure 8 also shows that the independent sector contribution to different security levels differed considerably. (The bars on this chart are shaded to reflect the NHS and independent sector components distinctly). Overall 22% of A&T patients in general wards, 49% in low secure and 59% in medium secure wards were in independent sector placements. This contribution varied significantly with age in general- and medium secure, but not low secure care (combined Kolmogorov-Smirnov D values for age distribution of independent sector compared to NHS patients general wards: D=0.1358, p<0.0001; low secure: D n.s.; medium secure: D=0.1171, p=0.10) There was a small but significant shift in the age profile of patients over the five years, with an increase in the proportion aged under 24 from 20% of patients in the first two years to just over 27% in 2010 (Figure 10 and Table 5). There was a small corresponding fall in those aged 45 and over from 28% to 25% of the total. These changes were seen in both independent and NHS sector patient groups. 15

27 Number of patients Figure 10. Numbers of A&T patients by age group in each census year plus to to to to to 24 0 to Year Table 5. Numbers and proportions of A&T patients by age group in each census year Census year 0 to to to to to to plus Total Overall proportion 6 (0.5%) 9 (0.6%) 15 (1.0%) 12 (0.9%) Percentages are by row. 239 (19.2%) 292 (18.8%) 403 (25.7%) 356 (26.5%) 322 (25.9%) 428 (27.6%) 411 (26.2%) 311 (23.1%) 330 (26.5%) 427 (27.5%) 378 (24.1%) 332 (24.7%) 208 (16.7%) 247 (15.9%) 234 (14.9%) 216 (16.1%) 96 (7.7%) 120 (7.7%) 99 (6.3%) 79 (5.9%) 42 (3.4%) 30 (1.9%) 29 (1.8%) 38 (2.8%) 0.7% 22.6% 25.8% 25.7% 15.9% 6.9% 2.4%

28 Number of patients Number of patients Legal status Figures for legal status under the Mental Health Act, across all ward types combined, showed important trends over the period, with an overall 36% decline in the number of informal patients, and overall increases of 2% and 13% respectively of patients detained under civiland criminal orders. The A&T figures shown reflect this trend, but are somewhat confused by the changes in ward classifications that produced the sharp, and almost certainly spurious rise and fall in overall A&T numbers shown in Figure 11 and Figure 12. Figure 11. Numbers of A&T patients by legal status and year Criminal orders Civil orders Informal Year Figure 12. Numbers of A&T patients by provider sector, legal status and year Criminal orders Civil orders Informal Independent NHS 17

29 Figure 12 illustrates the pattern of legal status for patients in independent and NHS sector providers separately (numbers are also given in Table 6). Just over a quarter of the patients in independent sector hospitals were informal, compared to just under half of those in NHS hospitals. In both sectors, roughly three fifths of detained patients were under civil orders, two fifths under criminal orders. Table 6. Numbers and proportions of A&T patients by legal status in each census year, overall and for independent and NHS sector providers. Overall Total Informal Civil orders % 38.6% 33.0% 39.2% 38.8% % 38.0% 38.1% 36.8% 37.4% Criminal orders 17.7% 23.4% 28.9% 24.0% 23.8% Total Chi square = 67.3, df = 6, p< Independent Total Informal Civil orders % 25.3% 25.3% 29.6% 26.5% % 46.8% 43.2% 43.0% 45.8% Criminal orders 24.1% 27.9% 31.5% 27.5% 27.7% Independent total Chi square = 13.2, df = 6, p<0.04 NHS Total Informal Civil orders % 46.5% 37.8% 46.3% 47.5% % 32.7% 35.0% 32.2% 31.5% Criminal orders 11.6% 20.7% 27.2% 21.5% 21.0% NHS total Chi square = 122.6, df = 6, p< Percentages by column table and accompanying charts omit 34 patients with ambiguously reported legal status. 18

30 Security levels in A&T care Information about security levels was not available for 2006 or For the remaining years, 42% of placements were in general (non-secure) wards, 40% in low-, 17% in medium- and 1% in high-secure wards. Data for A&T wards in isolation (Table 7) suggest an increasing proportion of non-secure provision, and declining proportions of low- and medium secure. This trend should probably be considered with caution as it runs counter to the data for all in-patient beds and may be an artefact arising from the variable ward coding patterns. There is a clear difference between the independent and NHS sectors in security patterns for A&T patients, with only 23% of independent sector patients in general (non-secure) provision compared to 54% of NHS sector patients (see Table 7; chi square = 435, df = 2, p<0.0001). These trends are illustrated in Figure 13. The chi square values shown on the table indicate that the variations between years are statistically significant in each sector and in the sectors combined. However, the patterns are complex. The proportion of non-secure placements in the independent sector rose sharply and then fell slightly; amongst NHS placements it fell slightly then rose sharply. Table 7. Numbers and proportions of patients in non-, low and medium secure placements for each of the three censuses, overall and separately in the independent and NHS sectors. Overall Total General Low % 41.7% 45.5% 42.0% % 39.8% 39.4% 40.6% Medium 18.2% 18.5% 15.1% 17.4% Total Chi square = 14.7, df=4, p=0.005 Independent Total General Low % 30.1% 24.4% 23.4% % 40.6% 54.8% 50.5% Medium 27.7% 29.4% 20.9% 26.0% Independent total Chi square = 53.5, df=4, p<0.001 NHS Total General Low % 49.0% 61.7% 54.2% % 39.3% 27.6% 34.1% Medium 12.4% 11.8% 10.7% 11.7% NHS total Chi square = 30.9, df=4, p<

31 Total patients in A&T placements Figure 13. Numbers of A&T placements by sector and security level Independent NHS Year and security level 20

32 Proportion of patients 4.4. Duration of stay How long people stay in hospital is an important issue but the scope of the available data was not ideal for exploring this. The length of time each current patient had been in hospital was recorded in each census, but the number of patients admitted to the hospitals in past periods, of which these were the remainder was not, and individual patients could not be tracked from census to census. For A&T patients, across all the four censuses where these could be identified, the median duration of stay was 16 months. Figure 14 shows, for each time period from 0 to 60 months (five years) the proportion of A&T patients who had been in hospital for that time or longer for each census year. These show statistically significant differences (log rank chi square = 60.2, df=3, p<0.0001). The chart shows that the group of patients found in the first census had had a shorter range of stays than for any subsequent census (medians 2006: 11.5 months, 2007: 19, 2009: 18 and 2010: 16). Overall, in the most recent census, 55% of patients had been in hospital a year or more, and 38% two or more years. Figure 14. Duration of stay of A&T patients at time of census, by census year. 100% 90% 80% 70% 60% 50% 40% 30% 20% % 0% Stay at time of census is at least time indicated Lines shows the proportion of patients in each census who had been in hospital for the time shown on the horizontal axis or longer. 21

33 Proportion of patients For analyses including security level, only three of these years could be used. Duration of stay in these years (2007, 9 and 10) varied significantly with security level (Figure 15). Median duration of stay at the time of census was significantly shorter for patients in non-secure accommodation (9 months),than for those in low secure (26 months) or medium secure (20 months (log rank chi square=213.4, p<0.0001). For patients in the two secure care categories, those in medium secure facilities had had shorter stays at the time of the census, than those in low. However this difference only emerged for stay durations in excess of two years (low vs. medium all durations, log rank chi square = 19.3, df=1, p<.0001; for durations less than 25 months, log rank chi square = 0.33, n.s.). Figure 15. Duration of stay at time of census, A&T patients by security level 100% 90% 80% 70% 60% 50% 40% 30% General Low Medium 20% 10% 0% Stay at time of census is at least time indicated 22

34 Proportion of patients As security level is clearly an important influence, it is not appropriate to compare durations of stay between the NHS and independent sectors without allowing for this. Figure 16 expands the analysis in Figure 15 by showing separate independent and NHS sector lines for each security level. For general wards, NHS durations of stay are highly significantly shorter (log rank chi square = 161.8, df=1, p<.0001). For low secure wards NHS durations of stay are longer but the difference is not quite statistically significant (log rank chi square = 3.68, df=1, p=0.055). For medium secure wards, independent sector patients had shorter stays overall (log rank chi square = 18.5, df=1, p<0.0001), but the difference was only apparent where stays exceeded a year. Figure 16. Duration of stay at time of census, A&T patients by security level and type of provider 100% 90% 80% 70% 60% 50% 40% 30% 20% Ind General NHS General Ind Low NHS Low Ind Medium NHS Medium 10% 0% Stay at time of census is at least time indicated We found three studies which looked at stay length in people with learning disabilities admitted to psychiatric in-patient care. Xenitidis and his colleagues were comparing psychiatric admissions in a new unit specifically providing for people with learning disabilities with admission of people with learning disabilities to general adult psychiatric wards. They found mean durations of stay for discharged patients of 23 weeks in the former and 11 in the latter. 13 Saeed and his colleagues in Kingston Ontario, looked at psychiatric admissions of people with developmental disabilities compared to others using survivorship statistics, These were however acute admissions and their groups had a median stay durations of 21 and 16 days respectively. 14 The most evidently similar study was an (as yet) unpublished audit study reported to us by Roy, looking at mean durations of stay at discharge of patients with learning disability treated in A&T units in the West Midlands, A total of 46 patients were discharged over three years, the most recent ending in March Mean durations at discharge were 399 days in the first year, but 676 and 653 days respectively in the second and third

35 Our analysis of durations of stay at different levels of security is complicated by the fact that we have no information about whether patients had been moved between levels of security. It is likely that units were answering the question how long has the patient been on this ward? In some cases this may have come about through a step up or down from a less or more secure level of care. Thus it is possible that the time the patient had been on the ward would not reflect the total duration of their time in hospital. 24

36 4.5. Patient experiences Simple analyses Five items in the census document the frequency of recorded incidents of adverse patient experiences. The types of incident were specified for the census as: self-harm, accident, physical assault on the patient, hands on restraint, and seclusion. Given the scope and organisation of the census it would have been unrealistic to ask for anything beyond details of what was recorded in patients clinical case-notes, and the census instructions confirm this was the approach taken. The question asked was How many of each of the following types of incident have been recorded for this patient during this hospital spell, or within the last three months (if the hospital spell is longer)? 16 The following analyses use only data for A&T units. For consistency, only data from the 2007, 2009 and 2010 have been used in the analyses shown and 2008 data are omitted because security level data are not available for either and ward type data are not available for Where possible, parallel analyses were undertaken also using the 2006 data. Results were not substantially different. Figure 17 shows the overall frequency of occurrence of these events to patients reported in the censuses. Actual frequencies were not recorded after 2006; instead providers reported frequencies in the groups which would be used for publication. 35% of patients in A&T units experienced at least one assault, and 41% at least one episode of restraint. 22% had at least one accident and 7% at least one experience of seclusion. 27% had one or more episodes of self-harm. Smaller proportions of patients were reported as having experienced these events repeatedly. The highest category of frequency used for reporting in most years was 10 times or more. This means the experiences had happened roughly once a week or more frequently. 6% had suffered at least 10 assaults, 10% had been restrained, and 1% secluded at least 10 times, 7% had had at least 10 episodes of self-harm and 2% at least 10 accidents. In nonsecure accommodation, these experiences were all significantly more frequent in patients who had been in their placement for longer than a year. In low-and medium secure settings, with one exception there was no significant difference in frequencies for longer standing patients. 1 Thus these are not phenomena characterising, or confined specifically to the early phase of assessment before units become familiar with patients. Analyses of these frequencies were published in spreadsheets accompanying the main report each year although, reflecting the purpose of the survey, the only analyses presented were 1 The exception was that in medium secure units seclusion was marginally less common in patients of more than a years standing (chi square 11.1, df=4, p=0.025). 25

37 by ethnic group; ward types were not analysed separately. Separate tabulations were produced for each Strategic Health Authority. Seclusion was more common in more secure settings, whilst accidents and assaults were less common. The frequency of any self-harm was similar between security levels, but the frequency of large numbers of episodes was greater for patients in non-secure settings. The frequency of restraint did not vary significantly between security levels. None of the types of experience documented showed a sustained trend over time. Experience of all but one of these types of event was less common for patients looked after by NHS than by independent providers. The exception was accidents. Differences were highly statistically significant. Figure 18 explores whether the difference between provider sectors could be attributed to the differing profile of security. It could not. The betweensector comparison is presented for each type of experience at each security level. Eleven out of the fifteen comparisons showed statistically significant differences, all but one favouring patients in NHS provision. The exception was seclusion in general (non-secure) wards, experienced by 3% of NHS patients but by less than 1% of patients in independent sector units. 26

38 Patients Patients Patients Figure 17. Patient experience variables overall frequency and variation between security levels and provider types (2007, 2009 and 2010) 5,000 4,500 4,000 Overall 3,500 3,000 2,500 2,000 1,500 1, Seclusion Accidents Assaults Restraint Self Harm 10 or more 5 to 9 2 to % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Variation with security (non-, low, medium) 10 or more 5 to 9 2 to % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Variation with provider type 10 or more 5 to 9 2 to Key. Significance level for variation between secure levels or provider types: * p<0.05, ** p<0.01, *** p<.001, ns not significantly different 27

39 Table 8. Patient experience variables overall frequency and variation between security levels (2007, 2009 and 2010) Experience and sector Seclusion Accidents Assaults Restraint Selfharm Frequency of occurrence to 4 5 to 9 10 or more Total Non-sec 97% (1808) 1% (20) 0% (8) 0% (9) 1% (12) 1857 Low sec 93% (1681) 2% (44) 2% (37) 1% (20) 1% (16) 1798 Med sec 81% (620) 6% (48) 7% (55) 3% (25) 3% (22) 770 Total 93% (4109) 3% (112) 2% (100) 1% (54) 1% (50) 4425 Non-sec 72% (1343) 13% (250) 8% (155) 3% (53) 3% (56) 1857 Low sec 81% (1463) 12% (208) 5% (91) 1% (25) 1% (11) 1798 Med sec 83% (638) 12% (90) 5% (35) 0% (3) 1% (4) 770 Total 78% (3444) 12% (548) 6% (281) 2% (81) 2% (71) 4425 Non-sec 62% (1158) 13% (245) 10% (182) 6% (117) 8% (155) 1857 Low sec 65% (1177) 12% (214) 11% (202) 5% (94) 6% (111) 1798 Med sec 70% (539) 16% (125) 7% (54) 4% (34) 2% (18) 770 Total 65% (2874) 13% (584) 10% (438) 6% (245) 6% (284) 4425 Non-sec 58% (1079) 11% (204) 12% (230) 7% (124) 12% (220) 1857 Low sec 59% (1065) 11% (205) 13% (227) 7% (130) 10% (171) 1798 Med sec 58% (446) 13% (101) 11% (88) 8% (62) 9% (73) 770 Total 59% (2590) 12% (510) 12% (545) 7% (316) 10% (464) 4425 Non-sec 73% (1349) 7% (132) 6% (118) 4% (81) 10% (177) 1857 Low sec 73% (1313) 8% (143) 8% (150) 5% (91) 6% (101) 1798 Med sec 75% (579) 8% (61) 8% (61) 4% (29) 5% (40) 770 Total 73% (3241) 8% (336) 7% (329) 5% (201) 7% (318) 4425 Chi Square (df=4) 242.5, p< , p< , p< , ns 33.5, p< Table 9. Patient experience variables overall frequency and variation between provider types (2007, 2009 and 2010) Experience and sector Frequency of occurrence to 4 5 to 9 10 or more Total Chi Square (df=4) Seclusion Accidents Assaults Restraint Self-harm Indep 90% (1580) 3% (54) 3% (56) 2% (36) 2% (32) 1758 NHS 95% (2529) 2% (58) 2% (44) 1% (18) 1% (18) 2667 Total 93% (4109) 3% (112) 2% (100) 1% (54) 1% (50) 4425 Indep 78% (1370) 11% (196) 7% (129) 2% (32) 2% (31) 1758 NHS 78% (2074) 13% (352) 6% (152) 2% (49) 1% (40) 2667 Total 78% (3444) 12% (548) 6% (281) 2% (81) 2% (71) 4425 Indep 60% (1053) 13% (225) 12% (214) 7% (118) 8% (148) 1758 NHS 68% (1821) 13% (359) 8% (224) 5% (127) 5% (136) 2667 Total 65% (2874) 13% (584) 10% (438) 6% (245) 6% (284) 4425 Indep 49% (862) 11% (188) 15% (271) 10% (171) 15% (266) 1758 NHS 65% (1728) 12% (322) 10% (274) 5% (145) 7% (198) 2667 Total 59% (2590) 12% (510) 12% (545) 7% (316) 10% (464) 4425 Indep 66% (1152) 9% (151) 11% (190) 6% (108) 9% (157) 1758 NHS 78% (2089) 7% (185) 5% (139) 3% (93) 6% (161) 2667 Total 73% (3241) 8% (336) 7% (329) 5% (201) 7% (318) p< p= p< p< p<

40 Patients Patients Patients Figure 18. Patient experience variables overall frequency of occurrence to individuals and variation between security levels and provider types (2007, 2009 and 2010) General (non-secure) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 10 or more 5 to 9 2 to Low secure 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 10 or more 5 to 9 2 to Medium secure 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 10 or more 5 to 9 2 to Key. Significance level for variation between provider sectors: calculated by chi square tests; * p<0.05, ** p<0.01, *** p<.001, ns not significantly different 29

41 % of patients % of patients % of patients % of patients % of patients Variations between provider units The frequency of all five types of experience varied considerably between individual provider units. Figure 19 shows bar charts, for each type of experience, comprising one bar for each of 103 provider units (6 were omitted because of having fewer than 5 patients). The overall height of the bars indicates the proportion of patients in the unit for whom at least one instance of the experience concerned was recorded within the three months prior to the census. Providers are grouped according to the type of security level care they offer (nonsecure, low, medium and mixed security levels). The gap with no apparent bars to the right hand side of each group of providers represents units where no patient had had the experience recorded. In some cases, the bottom section of bars is shaded more darkly. This represents the proportion of patients who had 10 or more instances of the experience. Figure 19. Proportions of patients in provider units with at least one and 10 or more reported instances of each type of experience (see text). Accident Assault 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 1 to 9 times 50% 1 to 9 times 40% 10+ times 40% 10+ times 30% 30% 20% 20% 10% 10% 0% Non-secure Low Med Mixed Providers grouped by security level 0% Non-secure Low Med Mixed Providers grouped by security level Restraint Seclusion 100% 100% 90% 90% 80% 80% 70% 70% 60% 60% 50% 1 to 9 times 50% 1 to 9 times 40% 10+ times 40% 10+ times 30% 30% 20% 20% 10% 10% 0% Non-secure Low Med Mixed Providers grouped by security level 0% Non-secure Low Med Mixed Providers grouped by security level Self Harm 100% 90% 80% 70% 60% 50% 40% 1 to 9 times 10+ times 30% 20% 10% 0% Non-secure Low Med Mixed Providers grouped by security level 30

42 The differences between units operating at different security levels could mostly be anticipated. For many units, the confidence intervals for these proportions are quite wide, reflecting small numbers of patients. The chart shows that seclusion is a technique used in only a minority of units, presumably as a matter of clinical policy, and in most of those, sparingly. Seclusion was strongly associated with unit security level. Thus 76% of units providing only general (non-secure) care reported no use of seclusion, whilst the corresponding proportions for low and medium secure units were 56% and 17% respectively (chi square = 24.8, df=2, p<.0001). There was no significant difference in this between the NHS and independent sectors. A key question is to what extent variations between units reflect different reporting thresholds or differences in completeness of note keeping. The available evidence simply does not allow us to answer this question. Unit scores for the different types of experience were in most cases significantly, though not strongly correlated. Table 10 shows rank correlations between the proportions of patients in each of the 103 units for whom each type of experience was reported at least once. The only pairing not significantly correlated was accidents and seclusion. Table 10. Spearman rank coefficients and significance levels to show correlation between proportions of patients reported as having at least one instance of each of the five types of patient experience Assault Accident Assault Restraint Seclusion (p=0.0001) Restraint (p=0.0090) (p<0.0001) Seclusion (p=0.3983) (p=0.0162) (p=0.0008) Selfharm (p=0.0027) (p<0.0001) (p<0.0001) (p=0.0196) 31

43 Combining the factors influencing patient experiences There are evidently complex inter-relationships between the level of challenging behaviours (for which the only direct measure we have is self-harming), the presence of legal compulsion, the level of security in which individuals are held, and the type of provider unit. In this type of situation, multivariate statistical analyses allow all the influences for which measures are available to be considered together. This allows the contribution of each to be weighed in the context of all the others. We addressed two questions using this approach: What combination of all the factors for which we had measures most effectively predicted the occurrence of the adverse patient experiences, and Which, if any, providers stood out as making a significant positive or negative contribution after allowance was made for all these factors? To do this we used generalised linear modelling, taking, as outcomes to be predicted, four of the experience variables; accidents, assault, restraint and seclusion. We categorised individuals in the census on the basis of whether or not they had experienced each on any occasion in the three months prior to the census day. As potential predictors, we used patients ages, genders, durations of stay to census (in months up to a ceiling of 120), legal status at the time of the census, ratings for autism and for mobility disability (present or absent), the level of security of their ward placement (general, low-secure or medium secure) and the provider type (independent or NHS) of the unit in which they were staying. After some discussion, we decided for modelling purposes to consider self-harming behaviour as an independent characteristic of patients, and hence a predictor of whether they were likely to experience seclusion, restraint, accidents or assaults, rather than a consequence to be predicted by other variables. In the first stage of the modelling, we developed eight baseline models, two for each outcome, one with and one without the provider type indicator. This was to allow us to explore both the general influence of each provider type, and subsequently the impact of individual providers units without making prior allowance for general influences (should there be any) of providers of their type. The second stage of the modelling looked at the effects of individual providers. We wanted to examine the individual influence of each of the 109 providers active in the three years for which all the relevant data were available, on equal terms. Most approaches to multivariate modelling of this type do not exactly do this. For example if a model is developed for each of the providers with all the other variables included, because the extent to which the chosen provider predicts the outcome under consideration varies, the contribution of the other variables in the final model will also vary. Thus we chose to make a best possible prediction of the chances of each outcome for each individual, based on the baseline model (excluding provider type) developed in the first stage. We then calculated whether introducing dummy 32

44 variables for each provider unit was associated with significantly increased prediction of each outcome. This approach is called offset modelling. Results of the baseline models are shown in Table 11. Self-harming episodes were a consistently strong predictor of all four types of experience, raising the chances of restraint six-fold and of the other experiences threefold. Seclusion was strongly predicted by both detention under the Mental Health Act (more than six-fold for civil orders and more than four-fold for criminal orders) and also, independently, by being in a medium secure unit (sixfold). Accidents by contrast were 40% less likely in both low and medium secure units and restraint was less likely in people with mobility disabilities. Accidents were almost twice as likely in people with mobility disability. Autism was associated with a 25% increase in use of restraint. Older age was associated with significantly reduced likelihood of seclusion and restraint but only to a very small extent. Independent as opposed to NHS providers were associated with a 30% increase in the likelihood of assaults and 60% increase in the likelihood of restraint after allowing for all these other factors. One expert reviewing these models raised the question of whether independent sector provision in the first-stage model 1 could in fact be standing as a proxy for provision out of area. Whilst not excusing the greater use of restraint or the increased risk of assault, this could suggest different types of explanation. The data available in the census about whether patients were placed at a distance to their home are described in more detail in section 4.6 below. Briefly we were only meaningfully able to identify whether or not patients had been placed outside the strategic health authority area in which they normally lived, these data were not available for 2009 and were missing for roughly 40% of patients in A&T units in 2006 and 2007 and 30% in (Ward type data were completely missing for 2008 so this analysis was not possible anyway). Thus the number of patient placements available for analysis was only 46% of that available for the analysis shown in Table 11. In this reduced set of records independent sector placement was moderately, but statistically significantly, correlated with placement outside home strategic health authority (pairwise correlation coefficient =0.28, p<0.0001). Re-running the analyses shown as Model 1, ward security level ceased to be a significant predictor of risk of either assault or restraint, and independent sector provision was associated with a greater excess risk of assault (62% excess) and a lesser excess risk of restraint (30% excess). Adding an additional dummy variable for placement outside home strategic health authority area to these models had little effect on their overall predictive capacity, and the new variable did not contribute significantly to either (Log-likelihood comparisons of model without and with outside home SHA variable: Restraint: LR = , Chi-square P-value = 0.72; Assault: LR = , Chi- 33

45 square P-value = 0.59). In both cases the effect was to increase slightly the odds ratio associated with independent sector provision. 2 2 By contrast, if the model was set up initially including the outside home SHA variable but not the organisation type variable, adding organisation type significantly improved the fit of the models for restraint and assault, though not accidents or seclusion (Log-likelihood comparisons of model using outside home SHA variable, without and with organisation type variable: Restraint: LR = , Chisquare P-value = 0.051; Assault: LR = , Chi-square P-value = ). 34

46 Table 11. Baseline model results, odds ratios and significance levels for predictors, for prediction of patients reporting at least one instance of each type of patient experience (see text for explanation). Model 1 including organisation type Seclusion Accidents Assault Restraint (Intercept) *** *** *** *** Independent provider (vs NHS as baseline) ns ns *** *** Compared to General wards: Low secure ** *** * *** Medium secure *** *** ** ns Female (vs male) ns ns ns ** Age (years) *** * ns *** Compared to Informal: Civil order *** ns * *** Criminal order *** ns ** ns Duration of stay to Census (months) ns *** ** ns With autism ns ns ns ** With mobility disability ns *** ns ** With self-harm *** *** *** *** Model 2 not including organisation type Seclusion Accidents Assault Restraint (Intercept) *** *** *** *** Compared to General wards: Low secure ** *** ns * Medium secure *** *** ns ns Female (vs male) ns ns ns ** Age (years) *** * ns *** Compared to Informal: Civil order *** ns ** *** Criminal order *** ns ** ns Duration of stay to Census (months) ns *** *** ns With autism ns ns ns ** With mobility disability ns *** ns *** With self-harm *** *** *** *** Significance codes: *** p<0.001; ** p<0.01; * p<0.05; ns not significant. 35

47 As noted, there were 109 provider organisations for which all relevant data were available. In exploring the contribution of each of these, we used Model 2 to provide offset risk estimates for each individual patient. This provided an equal comparison for all providers. Organisations could contribute as significant predictors in models by being associated with either an increased or a decreased likelihood of individuals being recorded as having the four types of patient experience. Ten (9%) organisations were associated with an increased probability of patients experiencing an accident, 13 (12%) an assault, 11 (10%) restraint, and 16 (15%) seclusion. Six (6%) organisations were associated with a decreased probability of patients experiencing an accident, 10 (9%) an assault, 9 (8%) restraint, and 3 (3%) seclusion. These numbers are higher than expected by chance alone, though not markedly. Table 12 shows the numbers of organisations associated with one or more types of experience overall and by provider sector. Seven providers had significant associations in both directions. Table 12. Number and percentage of providers showing significant odds ratios for increased or decreased likelihood of patients having reported types of experience, or no significant effect overall and by provider sector. Number of experience types Increased chance Decreased chance No significant effect Overall 4 0 (0%) 0 (0%) 54 (50%) 3 1 (1%) 1 (1%) 35 (32%) 2 7 (6%) 5 (5%) 17 (16%) 1 33 (30%) 15 (14%) 3 (3%) 0 68 (62%) 88 (81%) 0 (0%) Total units Independent 4 0 (0%) 0 (0%) 20 (43%) 3 1 (2%) 1 (2%) 14 (30%) 2 4 (9%) 3 (6%) 11 (23%) 1 15 (32%) 7 (15%) 2 (4%) 0 27 (57%) 36 (77%) 0 (0%) Total units NHS 4 0 (0%) 0 (0%) 34 (55%) 3 0 (0%) 0 (0%) 21 (34%) 2 3 (5%) 2 (3%) 6 (10%) 1 18 (29%) 8 (13%) 1 (2%) 0 41 (66%) 52 (84%) 0 (0%) Total units This analysis needs to be interpreted with caution. It is simply a description of predictive patterns in the information as reported in the censuses. Organisations may have varied in their thresholds for recording incidents in case-notes, and in the completeness with which they subsequently reported them in census returns. This may have been purposeful or may simply have reflected differences in the quality and completeness of note-keeping. There 36

48 may have been differences in the type and degree of disturbance or disability of patients sent to particular hospitals which are not reflected in the data available to us. Contracts through which patient care was organised and funded may have varied in the weight they gave to individual patient disturbance. If so, this may have given some providers a general incentive to emphasise the degree of disturbance of the patients they were caring for. Whilst the census was anonymous, this general bias could have carried through into reporting for it. Larger providers would have had a greater chance that any given level of divergence from average behaviour (positive or negative) would be identified statistically, simply because with larger numbers of cases, smaller deviations are statistically significant. This effect would operate in both directions. Finally, it quite possible the nature and quality of care provided in these hospitals has changed since the time the censuses were conducted, between two and six years ago. However a further possibility is that the findings may reflect real differences in the quality of patient care at the time of the censuses. If so, it is also possible that these differences may persist. We cannot publish details about individual units because providers were assured when they were invited to participate in the censuses that they would not be identified in any publications. For the reasons given above, we do not in any case consider them to be sufficiently dependable in relation to individual providers for this to be appropriate in view of the evident sensitivity. However, erratic reporting by individual providers is likely to obscure rather than to produce general patterns, thus we believe that the overall patterns that did emerge with substantial statistical significance are worthy of note. 37

49 4.6. Commissioning Completeness of recording The census asked about the organisation commissioning each placement. These were coded with varying degrees of completeness. Data for 2009 were not preserved at all, and interpretation for earlier years is complicated in a few cases by changes in PCT boundaries and other NHS administrative arrangements. In cleaning this field, where a former NHS commissioning organisation was identifiable, we substituted the current equivalent NHS commissioner. Table 13 shows a broad categorisation of commissioners as recorded in the censuses. The proportion of patients for whom no commissioning organisation was given fell from 17% in 2006 to 6% in 2010, the proportion for whom a conventional PCT or commissioning Care Trust could be identified remained fairly steady at 70%. A further 4% were reported as having placements commissioned by a Strategic Health Authority specialist commissioning group or a Strategic Health Authority.. A reasonably steady 12% of patients had a care provider organisation reported as the commissioner for the service. In 2010, for three quarters of these patients the provider and the reported commissioner were the same organisation, suggesting that the data had been incorrectly reported. Table 13. censuses. Numbers (and proportions) of patients by type of commissioner, all patients in four Year PCTs or Care Trusts 3227 (70%) 2904 (70%) 3057 (74%) 2594 (71%) Regional groups StHAs 44 (1%) 8 (0%) 30 (1%) 4 (0%) NHS Specialist Commissioning 0 (0%) 0 (0%) 1 (0%) 157 (4%) Regional total 44 (1%) 8 (0%) 31 (1%) 161 (4%) Providers Independent 283 (6%) 317 (8%) 378 (9%) 322 (9%) NHS Trust 105 (2%) 121 (3%) 163 (4%) 119 (3%) Providers total 388 (8%) 438 (11%) 541 (13%) 441 (12%) Local Authority 5 (0%) 5 (0%) 6 (0%) 30 (1%) Welsh or Northern Irish authority 170 (4%) 169 (4%) 157 (4%) 200 (5%) Unknown 775 (17%) 629 (15%) 315 (8%) 216 (6%) Total

50 Ward types The proportion of patients for whom a reasonable commissioner assignment was provided varied between types of in-patient unit. This is shown in Table 14. In earlier years, A&T units reported this less well. By the 2010 census the reporting rate for these units was close to the average. Campus NHS retained beds reported commissioners more completely. Table 14. Proportions of patients with PCT / commissioning Care Trust or regional organisation reported as commissioner by ward type and census year. Ward Type Acquired brain injury 1/1 (100.0%) A&T 740/1246 (59.4%) 974/1553 (62.7%) 915/1344 (68.1%) Campus NHS retained beds 430/480 (89.6%) 513/603 (85.1%) 460/466 (98.7%) High dependency 82/108 (75.9%) 94/101 (93.1%) 56/64 (87.5%) Intensive Care 1/11 (9.1%) 19/25 (76.0%) 17/17 (100.0%) Long-stay (more than a year) 1241/1631 (76.1%) 819/1147 (71.4%) 698/1061 (65.8%) Old long-stay 53/153 (34.6%) 6/79 (7.6%) 14/27 (51.9%) Other 179/293 (61.1%) 101/142 (71.1%) 66/84 (78.6%) Psychiatric Intensive Care Unit 7/35 (20.0%) - - Rehabilitation 78/156 (50.0%) 137/187 (73.3%) 181/362 (50.0%) Respite 176/190 (92.6%) 122/140 (87.1%) 126/132 (95.5%) Short stay (less than a year) 239/305 (78.4%) 119/176 (67.6%) 61/85 (71.8%) Total 3227/4609 (70.0%) 2904/4153 (69.9%) 2594/3642 (71.2%) To compare the usage of A&T units between different parts of the country we have presented numbers of patients per thousand people with learning disability. We took denominator figures from GP Quality and Outcomes Framework (QOF) prevalence data. We used these in preference to general population figures because they indicate that the adult prevalence of learning disability varies considerably between PCTs, with noticeable regional differences. 19 Most of these analyses relate only to the 2010 census, but we have used numbers known to GPs in 2010/11 as the denominator figure for all years as these appear likely to be the most complete. QOF learning disability prevalence data were first collected in 2006/7 and the numbers for this and the following year suggest substantial undercounting. QOF numbers reached a plateau, which we would take to be a reasonable reflection of the actual position in 2009/10. We anticipated that there might be some degree of substitution between other bed types and A&T units. We explored this by examining in parallel, rates for all other types of care provision roughly divided into longer and shorter-stay types of care. Table 15 shows the figures grouped to Strategic Health Authorities for the most recent year (2010). A parallel set of data at PCT level are given in Annex 1. The table also show total beds per 1000 people with learning disabilities. The table footnote indicates how we grouped the Count-Me-In census ward types. 39

51 Table 15. Rates of use of inpatient care in 2010, per 1000 adults recorded by GPs as having a learning disability (with 95% confidence intervals), for A&T and other types of in-patient unit, for which a PCT or Strategic Health Authority Commissioner was identified, grouped by Strategic Health Authority. Strategic Health Authority A&Ts Other Non-Acute All Non-Acute Other including acute and respite North East 11.2 (9.4 To 13.2) 11.8 (10.0 To 13.9) 23.0 (20.4 To 25.9) 1.3 (0.8 To 2.2) 24.3 (21.6 To 27.3) North West 3.0 (2.4 To 3.7) 8.1 (7.1 To 9.3) 11.1 (9.9 To 12.5) 0.3 (0.2 To 0.7) 11.5 (10.2 To 12.8) Yorkshire and The Humber 4.4 (3.5 To 5.4) 4.3 (3.5 To 5.3) 8.7 (7.5 To 10.1) 2.7 (2.1 To 3.6) 11.5 (10.1 To 13.0) East Midlands 3.9 (3.0 To 5.0) 12.8 (11.2 To 14.6) 16.7 (14.9 To 18.8) 4.3 (3.3 To 5.3) 21.0 (18.9 To 23.3) West Midlands 7.7 (6.6 To 9.0) 6.9 (5.8 To 8.1) 14.6 (13.0 To 16.3) 3.5 (2.7 To 4.4) 18.1 (16.3 To 20.0) East Of England 6.9 (5.8 To 8.2) 7.8 (6.6 To 9.1) 14.7 (13.0 To 16.5) 1.3 (0.8 To 1.9) 16.0 (14.2 To 17.9) London 4.0 (3.2 To 4.9) 3.0 (2.3 To 3.9) 7.0 (5.9 To 8.2) 0.8 (0.5 To 1.3) 7.8 (6.7 To 9.1) South East Coast 2.8 (2.0 To 3.8) 6.1 (4.9 To 7.5) 8.9 (7.4 To 10.6) 1.3 (0.8 To 2.1) 10.3 (8.7 To 12.1) South Central 5.6 (4.4 To 7.1) 10.0 (8.3 To 11.9) 15.6 (13.5 To 18.0) 1.6 (1.0 To 2.5) 17.2 (15.0 To 19.7) South West 2.7 (2.0 To 3.5) 5.7 (4.7 To 6.8) 8.4 (7.2 To 9.7) 0.8 (0.5 To 1.3) 9.2 (7.9 To 10.6) England 5.0 (4.6 To 5.3) 7.3 (6.9 To 7.7) 12.3 (11.8 To 12.8) 1.8 (1.6 To 2.0) 14.0 (13.5 To 14.6) All Other non-acute comprises the following categories: Campus NHS retained beds, Long-stay, Old long-stay, and Rehabilitation ; Other including acute and respite comprises: Acquired brain injury, High dependency, Intensive care, Psychiatric intensive care unit, Respite, Short stay and unlabelled. 40

52 Rates of use of A&T beds ranged from 2.7 per 1000 people with learning disabilities in the South West to 11.2 in the North East. North West, Yorkshire and the Humber, East Midlands, London and the South East Coast areas all had rates lower than 5. The only low reporting area which showed notably high rates for other types of beds, which might possibly be substitutes, was the East Midlands which was reported to be commissioning relatively high numbers of patients in both other broad categories and overall had the second highest bed complement. Two other types of potential substitute provision are harder to quantify. It is possible that areas with lower numbers of in-patients in learning disability beds may have compensated with larger numbers in non-specialist mental health services or in prison. We have no data source that can shed light on the latter. However the Count-Me-In census does provide some information about the numbers of people in beds primarily for the treatment of mental illness who have a learning disability. Figure 20 shows the rates of in-patient care in the 2010 census by the Strategic Health Authority of the commissioner, including those in mental illness- as well as learning disability beds. There is no significant correlation between the rates of admission to mental illness and learning disability beds (Pearson correlation coefficient =-0.38, p=0.28). However it is apparent that London stands out with a much higher proportion of admissions in mental illness beds. If the London figure is omitted the correlation coefficient for the remaining 9 areas is Figure 20. Rates of in-patient care in both learning disability and mental illness beds, for people noted in Censuses, as having learning disability per 1000 population by Strategic health authority of commissioner and principal purpose of in-patient provision East Midlands East of England London North East North West South Central South East Coast MI beds LD beds South West West Midlands Yorkshire and the Humber Total in-patients (2010) per 1000 population with LD 41

53 The wide variation in the rate of use of A&T units between Strategic Health Authority areas conceals considerable diversity between PCTs within each, but also a considerable degree of clustering. Figure 21 shows the pattern of PCT rates for people in these facilities within Strategic Health Authority areas. PCTs have been divided into five equal sized groups (quintiles) on the basis of their usage rate. The group with the highest rates were using 8.5 beds per 1000 people with learning disabilities or more; the group with the lowest were using fewer than 1.2 beds per Two Strategic Health Authorities (South Central and South East Coast) had no representative in the highest quintile; three (South Central, West Midlands and North East) none in the lowest. London shows a particularly evenly spread range of bed-use rates. Figure 22 is a map of these data. From the information available, it is not possible to say whether these apparently wide divergences within Strategic Health Authorities may in some cases represent local lead commissioning arrangements. It is possible, particularly where PCTs are small, that one may undertake specialist commissioning on behalf of neighbours. Care providers reporting commissioning arrangements may have been unaware of these details. This could explain some instances where geographic neighbours show sharply different rates. For example, in London a surprisingly high number of patients was reported for Richmond and Twickenham PCT whilst for neighbouring Kingston there was none. However in many cases, notably the North East, South West, North West and East Midlands, there is a very substantial clustering of similar figures. The London map shows similar clustering at a smaller geographic scale. Figure 21. Numbers of PCTs in each quintile for rates of A&T bed use, 2010 census. The key shows the quintile boundaries as rates per 1000 adults with learning disabilities. South West (13) South Central (9) South East Coast (7) London (29) >8.5 East of England (12) to 8.5 West Midlands (17) East Midlands (9) to 5.15 to to 1.2 Yorkshire and the Humber (14) North West (23) North East (12) % 20% 40% 60% 80% 100% 42

54 Figure 22. Geographic pattern of bed use rates (persons per 1000 known to GPs with LD) in A&T units, 2010 census. Areas with no recorded usage are identified in white. 43

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