A cluster analysis of support networks of older people with severe intellectual impairment

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A cluster analysis of supportnetworks of older peoplewith severe intellectualimpairmentSteve Mossa & James Hogga

a Hester Adrian Research Centre, The University,Manchester, M13 9PL, United KingdomPublished online: 22 Jan 2014.

To cite this article: Steve Moss & James Hogg (1989) A cluster analysis of supportnetworks of older people with severe intellectual impairment, Australia and NewZealand Journal of Developmental Disabilities, 15:3-4, 169-188

To link to this article: http://dx.doi.org/10.1080/07263868900033651

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Australia and New Zealand Journal of Developmental Disabilities 1989,Vol 15, Nos. 3 &4, 169-188.

A CLUSTER ANALYSIS OF SUPPORT

NETWORKS OF OLDER PEOPLE WITH

SEVERE INTELLECTUAL IMPAIRMENT

STEVE MOSS AND JAMES HOGG

Hester Adrian Research Centre The Universi~

Manchester M13 9PL. United Kingdom

The report describes the results of a demographic survey of people with severe intellectual impairment over 50 years of age living in a single Metropolitan Borough (popn. 220,000) in the U.K. The survey sought to collect comprehensive information on a relatively small group of individuals, rather than covering a larger population in less depth, and which enabled the resulting analysis to elicit the complex patterns of interaction which exist betw~een service/informal support variables and variables describing characteristics of the clients themselves. Using a multivariate technique, it was possible to determine the overall structure of the data set, and to observe the significance of age and measures of functional ability within this overall structure. Apart from a number of variables which appeared to be relatively independent, seven clusters were identified, corresponding to a number of important domains within the lives of these individuals. The clusters related to (a) contact with relatives, (b) community activities, (c) community independence, (d) friendship, (e) service pathways, (f) geriatics/mobility, (g) physical treatment. Subsequent factor analysis of ~lected variables confirmed the existence of these clusters, and made it possible via the use of factor scores to assess the magnitude of the relationships between client descriptor variables (age and function) and the clusters in which they occurred,

In the U n i t e d K i n g d o m , as e lsewhere , service p rov iders p l a n n i n g services for o lde r peop le wi th severe in te l lec tua l i m p a i r m e n t ( men t a l r e t a rda t ion ) have b e e n h a m p e r e d by a lack o f accura te i n f o r m a t i o n o n the charac te r i s t i c s of th is popu l a t i on . In r e sponse to th is need the D e p a r t m e n t of H e a l t h in Br i t a in c o m m i s s i o n e d a d e m o g r a p h i c s tudy w h i c h would cons i s t o f a de ta i led survey o f the ent i re p o p u l a t i o n o f peop le with severe in te l lec tua l i m p a i r m e n t over the age of 50 years, l iv ing in, or o r i g i na t i ng f rom, a s ingle adm i n i s t r a t i ve dis tr ic t (Hogg & Moss , 1987). Th i s district , O l d h a m M e t r o p o l i t a n Borough , is a n indus t r i a l town in the N o r t h West of E n g l a n d , wi th a p o p u l a t i o n o f a b o u t 220,000. T h e full desc r ip t ion o f th is projec t a n d its f ind ings is g iven in four repor ts dea l ing with IQ a n d adap t ive b e h a v i o u r m e a s u r e s (Hogg & Moss , 1989), m a i n survey resul ts (Moss , Hogg & Horne , 1989), l oca t ion o f i nd i v i dua l s no t cur ren t ly in con tac t wi th m e n t a l h a n d i c a p services (Horne , 1989), a n d in terv iews wi th fami l ies o f those peop le l iv ing wi th pa r en t s (Prosser , 1989).

O u r focus was o n the co l lec t ion o f a de ta i led da t a set o n a relat ively smal l n u m b e r

The work reported was supported by a Department of Health research grant to the authors.

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170 A CLUSTER ANALYSIS

of individuals, rather than collecting more superficial information on a larger number of people. This, it was argued, would permit a more comprehensive analysis than has been presented in previous studies. The possession of a highly detailed data set makes it possible to investigate the patterns of interaction between various aspects of service provision, informal support and charateristics of the clients themselves. Some of these patterns of interaction, using mainly univariate analytic techniques, have been presented elswhere (Moss et al., 1989). Here, we take these analyses a step further using cluster analysis. Using this technique we were able to investigate the overall structure to the data set, relating characteristics of the individual to service and informal support contexts. By involving all the variables in a single analysis it was thus possible to take account of the many intercorrelations which exist, and which may be missed in a univariate approach.

Research Background

As a result of the growth of interest in the needs of people with severe intellectual impairment a sizeable literature has developed over the past 15 years (for reviews see Janicki & Wisniewski, 1985: Hogg, Moss, & Cooke, 1988). In particular, the survey of Janicki and MacEachron (1984) in the State of New York provided one of the largest demographic data sets currently available, furnishing information in a variety of areas including medical characteristics, services received and place of residence, Baker et al. (1977) and Seltzer and Krauss (1987) have conducted major surveys of residential provision and programming to elderly clients on a nationwide basis.

Results from the main surveys which have currently been reported tend to be presented in terms of categories and percentages: e.g x% live in hospital, y% have dementia, z% have a muscutoskeletal disorder. Findings of this kind, while provid- ing some general description of the population under consideration, are not very useful to service planners, because they do not provide any information on the relative importance of specific client, service and informal support variables. To make this information available it is necessary to provide analysis of the patterns of interaction between characteristics of the client and service/informal support variables, and to achieve this requires accurate, in-depth, information on each client. Achieving this quality of information necessitates a high degree of co- operation with service staff, a condition which is more easily met by a study of the kind reported here,

Policy Background

The survey was undertaken in a single Local Authority/District, Oldham Metropolitan Borough, in the North West of England. This Borough is within the North Western Regional Health Authority (NWRHA) in which the national policy of developing a community" care service (including hospital closure) is at present being implemented. The NWRHA has developed criteria for a Model District Service, premised on the overall objective of giving people with severe intellectual impairment an ordinary life in the community. In enabling individuals to avail themselves of services, the Regional policy does not discriminate against older people with severe intellectual impairment, i.e. they are deemed to be entitled, as are their younger peers, to the full range of services and to an ordinary life of good quality.

Within the UK, social work services for people with severe intellectual impairment are organised separately from services to other groups such as elderly people.

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MOSS & HOGG 171

These social services are under the control of Local Authorities. District Health Authorities, which are completely independent of Local Authority, also have a community role in relation to people with intellectual impairment, provided by community nurses. There is a considerable overlap between the roles of social workers and community nurses working in this area.

In most areas the Local Authority and Health Authority have established a multidisciplinary team known as a Community Mental Handicap Team (CMHT). Such teams provide specialist help and coordinate services within the local area. CMHT's usually consist of social workers, community nurses and other professionals such as clinical psychologists.

The Community Mental Handicap Team in Oldham operates as a specialist referral agency and does not attempt to offer a service to all people with severe intellectual impairment in, or originating from, the Borough. This situation has important consequences for the overall research strategy as it was necessary not only to collect information on people in contact with the CMHT, but also on people with severe intellectual impairment not in receipt of services or who were the responsibility of other agencies. The target population was taken to include not only people at present living within the Borough boundaries, but those originating from Oldham but living in longstay hospitals and elsewhere outside the Borough.

In defining the population, criteria related to severe intellectual impairment were specified, with an intelligence quotient of approximately IQ=50 being taken as a guide for inclusion. In addition, individuals 50 years of age or older were included, the aim being to look at"older individuals" rather than exclusively at elderly people. Choice of CA= 50 years is also broadly in line with a substantial proportion of the internationaI literature (though see Janicki & Hogg, this volume).

Method

The study included two major survey components: (i) An agency survey in which people with severe intellectual impairment over the age of 50 years were included; (ii) An Outreach exercise in which individuals meeting the stated criteria but not in contact with the CMHT were included (see Home, this volume). This entailed contact with non-CMHT statutory agencies, voluntary and community organisafions. Thfis, a genuine population survey was attempted which enabled comparison of individuals in receipt of specialist mental handicap input as well as those who had not received such a specialist service.

Two questionnaires were prepared and piloted, one for use by respondents concerned with people living in the community, the other devised to collect information on residents oflongstay hospitals. (These will be henceforth be referred to as the HARC Aging Questionnaires). The coverage of the questionnaires was extensive, embracing personal details (age, etc.), residential information, history of residential relocations and planned resettlement, employment and day services, education and training, leisure, informal social networks and legal aspects. This information was collected on members of the CMHT sample as well as, wherever possible, those identified through Outreach. Medical and psychiatric information was not included in this survey but is the subject of another study.

In addition, detailed information,on the lives of population members living at home with relatives was undertaken through the use of a specially devised family questionnaire (Prosser, 1989).

Information on the abilities of population members was collected through the Adaptive Behavior Scales (Parts 1 and 2) (ABS), completed by stafffamiliar with the individual. In addition, where possible, a psychometric assessment was carried out

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through direct testing employing an appropriately modified version of the Kaufman Assessment Battery for Children (K-ABC). Psychometric assessments are not discussed here,

Results

Factor analysis of the ABS data

Hogg & Moss (1989) report the detailed statistical analyses undertaken to arrive at scores for each individual with respect to both adaptive and maladaptive functioning. With regard to the former, two dimensions were defined. The first of these was concerned with a variety of personal and social skills of which the most significant were: self-feeding, toilet use, cleanliness, self-dressing and initiative. We refer to this dimension as personal/social competence designating it as ABS 1 F1 (i.e. Adaptive Behavior Scale Part 1, Factor 1). The second dimension reflected w~der skills, notably verbal expression, comprehension, social language, understanding of number and time, and use of money. We have referred to this as community competence designated ABS1F2, noting that they approximate closely to those found in a more extensive study with respect to age-range by Nihira (1976).

Four dimensions of maladaptive behaviour were found when ABS Part 2 was analysed. Of these only the first, antisocial behaviour (ABS2F1) plays any significant part in the results reported in the present paper. This dimension is characterized primarily by antisocial behaviour, violent behaviour, rebelliousness and untrustworthiness. The second dimension was labelled personal maladaptation (ABS2F2) (i.e. stereotyped behaviour, bad vocal habits and hyperactivity). The third was withdrawal (ABS2F3) and the fourth, self-injury (ABS2F4).

Summary of Univariate Analyses

On closure of the data files, there was a total of 122 clients in our sample. Of these, 91 were in the "CMHT'" sample (74.6%) while the remaining 31 (25.4%) were located via the Outreach procedure (Home, 1989 and this volume). The main analyses (Moss et al., 1989) attempted to answer questions in relation to a number of specific themes:

Comparison of CMHT and Outreach samples Current residence Service support Informal networks Employment and retirement

As a result of these analyses the overall picture which emerged was of a population of people highly vulnerable to enforced moves. In the period 1983-1988, a substantial majority wilt have moved their home. Resettlement of hospital residents originating from Oldham back to Oldham constitutes a central aspect of NWRHA policy. In addition, resettlement within Oldham is also possible from hostels to more independent living, and also when a family can no longer cope. Thus, in the previous five years 36 per cent of the entire population had been resettled. In the CMHT sample, resettlement was primarily from the family home to another residence, usually to a residence run by the statutory services. The next most prevalent move was from a longstay hospital to ordinary housing in the community. Most moves in the Outreach sample were to Part III accommodation for elderly people. (Part III accommodation is Local Authority hostel accommodation for

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MOSS & HOGG 173

individuals requiring a high level of support whether elderly or with specific disabilities). Both proactive and reactive resettlement can be seen in these residential pathways. Resettlement from hospital is typically planned and proactive, reflecting policy. A move from the family home, however, is typically a reaction to the family's increasing difficulty in maintaining the person in their home.

Considering the nature of the residences to which people had been resettled, assessments of intellectual ability and community competence tended to be related to the characteristics of different living situations in a consistent fashion. Those in sheltered residences and those living independently were the most able with respect to all measures of such abilities. Those living with their family tended to be least competent, with hostel and hospital residents occupying an intermediate position. Where differences in competence were found, we can infer that they reflect selection processes that have gone on over some time. This is particularly likely with respect to those who live in unstaffed accommodation. Although the Model District Service recommends no discrimination on the basis of ability, it is clear that individuals being resettled tend to be more able. Given that the primary residential type in which they will be placed is residential accommodation, e.g., small group houses, it is then, likely that we will find more able people in such settings. The relatively lower ability of family members with respect to community competence may reflect a lack of opportunity to develop such competence, with the family acting in a relatively more protective role. However, the opportunity to learn basic self-help and domestic skills will be high in a family setting, and despite their lower intellectual status they perform best in this respect.

Superimposed on this pattern of residential provision are two subsamples of the present population, i.e. those in current contact with the CMHT and those not at present receiving this service. An important distinction between these two samples was that, while the CMHT group were by definition in receipt of mental handicap services, a substantial proportion of the Outreach group received provision for elderly people. Though the average age of the Outreach sample (69.7 years) was higher than the CMHT sample (61.4 years), there was no difference in intellectual level, personal/social competence or community competence. The only aspect of functional behavigur distinguishing the two groups was antisocial behaviour, which was higher in the CMHT sample.

In terms of residential provision, one of the main differences between these two samples was with respect to Part III accommodation. In the CMHT sample, all people in Part III accommodation were in mental handicap hostels; in comparison, eleven of the thirteen Outreach people in this category were in elderly people's accommodation. This difference had an important impact on the observed differences. First, the large number of Outreach individuals in Part III accommodation for elderly people was reflected in the greater average age of this group. Second, people in this type of accommodation appear to receive a lower 'level of service support in relation to individual programming, leisure activities and community involvement. Taken together, these effects manifested themselves as an apparent trend within the population as a whole towards a lower level of service with increasing age. In fact, this observation was almost totally explained by the CMHT/ Outreach differences in service level. There was no evidence for a reducing service input with age within either the CMHT or Outreach samples.

In all areas of social provision, i.e., individual treatment programs, education and leisure, members of the CMHT sample received a higher level of input, this despite no difference in the functional levels of the two samples. It is thus clear that future attention must be directed to the reasons why some people are in touch with

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the CMHT and some are not. Although our data were not directed towards answer- ing this question, one possible contributing factor emerged during analysis, namely, the association between receipt of services and the degree of antisocial behaviour exhibited. One important function of antisocial behaviour, we have suggested, is to draw the attention of service providers to that client, increasing the likelihood of active intervention.

Although contact with the CMHT is associated, in the analyses presented in this report, with a higher overall level of service input, it is clear that this greater level of support is not necessarily due to regular contact with a member of the Team. Within the sample of those people who were in touch with the CMHT, over half the individuals were reported as having contact very infrequently~ three times a year or less. People living alone or in group houses without 24 hour staffing received the majority of the CMHT's input. Support was intensive for people who have been resettled from hospital to community, although it was not clear whether this level of support was greater than for other community residents. In general, however, it appears that people receiving any of Oldham's mental handicap services, including residential provision, CMHT support, and Adult Training Centres (ATCs), tended to receive a better service than that provided by services for the general elderly population.

Each of the analytic themes drew on the entire data set as necessary. This was because, as discussed above, many of the variables were known to interact with each other, (e.g. services received and patterns of friendship both relate to the kind of residence in which the person lives). However, using mainly univariate analyses it was only possible to investigate a small subset of possible interactions between variables. These were determined largely by specific questions posed at the outset, and by the themes pursued during analysis. Clustering of the variables, presented below, complements this approach. The use of this multivariate technique made it possible to determine the overall structure of the data set, and to observe the significance of age and measures of functional ability within this overall structure. It will be shown that a large proportion of the variance can be accounted for by a relatively small number of clusters; the location of the client descriptor variables (age and function) help to clarify our understanding of the significance of client characteristics to service provision.

Cluster Analysis

Method

Cluster analysis, as applied in this case, operates on the correlations between all possible pairs of variables entered into the analysis. The process starts with all the variables separate, at the first stage linking the pair of variables which are "closest" together, (i.e. most highly correlated). Once a variable has been linked, it loses its separate identity within the clustering process, becoming "'agglomerated" into a cluster, Variables which are closely related to each other will tend to cluster at an early stage, while variables not closely related to any others will not link until the later stages of clustering. Similarly, clusters which are relatively uncorrelated with each other will not link until relatively late in the clustering procedure. Linkages are made between variables and clusters until all variables have become a single cluster. Thus, the clustering process for n variables requires n-1 steps. There are various methods for determining which is the next linkage to be made, the difference being partly a matter of the way in which the distance between clusters is measured. The mathematics involved in these clustering procedures is complex and

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MOSS & HOGG 175

inappropriate to discuss here. In some situations a specific linkage method is chosen for theoretical considerations relating to properties of the data set. In the present case, however, there was no obvious theoretical consideration guiding our choice of method. We therefore chose one of the most commonly used methods, average linkage, in which cluster proximities are measured between the centres of the clusters.

Apart from client variables, (age and function), variables entered into the analysis were selected from the full data set to cover all the principal domains of service, residence and informal support which were included in the HARC questionnaires. Some recoding was necessary for variables where some categories attracted hardly any responses. In particular, some of the nine-point frequency scales had very few responses which were any other category except 9 (i.e., "never"). These were recoded to become dichotomous variables, so that the activity was coded as either being present or absent (i,e., 1-8 = present, 9 = absent). Table 1 lists the variables included in the analysis, and also the key to their names as they appear in the subsequent clustering plots.

Table 1 Variables in the cluster analysis

NAME USED IN VARIABLE FIGURES

Client Characteristics: 1 Chronological age 2 Sex 3 Personal/Social competence 4 Community competence 5 Antisocial behaviour 6 Personal maladaptation 7 Withdrawal 8 Self injury

Residence: Community/Hospital Staffing level

Population sample: Known/Outreach

Service support: Frequency of contact with CMHT Frequency of direct contact with General Practitioner GP (repeat prescription) Number of individual programmes received (IPPs) Home Help Psychiatric Services Speech Therapy Audiology Opthalmology Physiotherapy Chiropody (Podiatry) Dentistry

AGE SEX ABS1F1 ABS1F2 ABS2F1 ABS2F2 ABS2F3 ABS2F4

COMMHOSP I N D E P E N D

KNOWNOUT

CMHTCONT GPDIRECT GPREPEAT NOOFPRGS HOMEHELP PSYCHIAT SPEECHTH AUDIOL OPHTHALM PHYSIO CHIROP DENTIST

Table continued on page 176

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Table 1 continued from page 175

NAME USED IN VARIABLE FIGURES

Occupational Therapy Dietician Wheelchair bound? Incontinent? Receiving treatment for a physical condition? Receiving treatment for a psych, condition? Use/Non-Use of the following aids:

Bathroom Toilet Dining Room Seating Mobility Bedroom Sensory Aids General Aids

Work/Retirement: Retirement on health grounds?

Education: At least one hour education per week?

Leisure Provision: Pub visits Cinema/Theatre Concerts Watching sport Care/Restaurant Shopping Frequency of unaccompanied trips Frequency of accompanied trips Clubs Sporting activities Staff input for additional leisure activities Received a holiday in the past five years?

Informal support: Contact with relatives? Visits to relatives with overnight stay Visit to relatives without staying Letters from relatives and friends Does the relative receive cards? Involved in a friendship scheme? Contact with friends who have mental handicap? Contact with friends who are nonhandicapped?

OCCTHER DIETICN WEELCHR INCONT PHYSTRT PSYCTRT

BATHAID TOILAID DINEAID SEATAID MOBILAID BEDAID SENSMD GENAID

HLTHRET

ANYEDUC

PUBFREQ CINEFREQ CONCRTFR SEESPTFQ CAFEFREQ SHOPFREQ ALONETRF TRWITHFQ GOCLUB DOSPORT STAFLES HOLSIN5

RELCONT RELSTFQ RELNOSTF LETTRFQ CARDFQ FRNDSCHM MHFRNDFQ NHFRNDFQ

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MOSS & HOGG 177

The residential variable relating to staffing level ( " INDEPEND") was a binary classification into 24 hour and less than 24 hour staffing. On the basis of findings presented earlier, that people living with their families were functioning at a relatively low level, these individuals were classified along with those people who were living in 24 hour staffed accommodation.

No other variables were included from the work/retirement section because the patterns of experience of hospital and community residence were so different that there were no meaningful compatible variables which could be derived for all clients in the data set.

There were some variables in the questionnaire which were excluded due to the fact that there were so few people in one of the categories. These variables included the services: Nightwatch, Meals on Wheels, Laundry, Health Visitor and District Nurse. (Meals on Wheels is an at-home cooked meal service provided by the Local Authority to people whose ability to cater for themselves is limited, usu ally because of infirmity). Also, the question on whether or not the client was bedridden was excluded due to the fact that none of the populat ion were categorized as such.

Results of Clustering

The icicle plot showing the pattern of linkages between the 61 variables is shown in Figure 1. Variable names appear along the top of the table, the contributions of individual variables being represented by the columns of crosses directly below the variable name. When a variable becomes linked to its neighbour, the column of crosses between the variable names also becomes filled in. Figure 1 is to be read from the bottom upwards, starting with all the variables independent and ending with all the variables l inked together at level 1. Note that the table shows the patterns of linkage from level 35 only.

Since the number of variables is 61, there are 60 clustering steps required to reduce all 61 variables to a single cluster, since one linkage is added at each step. When interpreting a clustering pattern it is most important to bear in mind that the number of steps always equals one less than the number of variables, irrespective of the magnitude of correlation between the variables. Broadly speaking, more highly correlated pairs of variables are linked at an earlier stage of the clustering than less correlated ones, An interpretation of the clusters cannot, however, be made without a knowledge of the magnitude and range of correlations of the variables. Within the data set considered here, there is a wide variation in the correlation matrix values. Also, several of the dichotomous variables categorise the vast majority of clients in one category and only a few in the other. "very small cell sizes such as this tend to produce unrel iable clustering. As a result, therefore, some of the associations make very little sense, These associations will be mentioned only in passing, the main focus of discussion being on the major patterns which emerged.

On the icicle plot in Figure 1 clustering level 10 has been picked out by an arrow at the lefthand side. At this level, three of the clustering groups of variables have been highlighted at the top of the figure. The variables included within these clusters relate closely to the individual analyses which have been presented above, Those variables which are n o t included in the clusters, are variables which were previously shown to relate to only a small proport ion of the population, These variables included: speech therapy, retirement on health grounds~ opthalmology, refusal to go on trips, home help, occupational therapy, audiology, sensory aids. visits to a relative involving an overnight stay and psychiatric treatment. One of the client variables, personal maladapta t ion (ABS2F2) is also outside the three clusters marked on the figure. This is due to the fact that levels of personal maladapta t ion

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MOSS & HOGG 179

within the population were extremely small. Cluster 1 in Figure 1 includes four variables relating to the client's contact with

relatives, meetings in the place of residence, visits to a relative involving no overnight stay, receipt of cards and contact with relatives (yes/no). From the position of cluster 1 with respect to the rest of the variables, we conclude that contact with relatives appears to be independent of a number of variables which might have been thought to have been related, notably age and residential type (i.e. hospital/ community and residential staffing level). These findings confirm those of the family study (Prosser, 1989).

We turn now our attention to cluster 2, a large cluster of activities which group under the broad category of community activities. This cluster is shown on a larger scale in Figure 2.

At clustering level 19 it can be seen that cluster 2 consists of one large cluste r on the left hand side (cluster 2A) and three smaller ones to the right of it (clusters 2B, 2C and 2D), these agglomerating into a single cluster by level 12. Note also that (a) sex remains an independent variable, not joining any other variable until level 15, and (b) the age of the client is included in cluster 2, specifically in cluster 2D involving two other variables: staff input to leisure activities and CMHT/Outreach sample. Cluster 2A on the left hand side contains a wide range of community activity variables such as going to clubs, pubs and cafes, accompanied trips, and holidays. Note also that attendance/nonattendance to the dentist is included in this group of variables although the reason for this inclusion is not entirely clear. At the right hand side of cluster 2A, anti-social behaviour (ABS2F2) and number of programs received are closely associated with each other (agglomerating at level 33), and also related to this general area of community activities. The specific direction of association between variables will be discussed later on in the context of the subsequent factor analyses, but it should be remembered in passing that the antisocial behaviour factor was found in the analyses presented earlier to be positively related to community involvement i.e. that higher levels of community activity were found to be associated with higher levels of behaviour problems.

Cluster 2B is related to the client's level of community independence, involving the three variables of(a) ability to go on trips alone, (b) frequency of contact with the CMHT, and (c) level of residential staffing. People categorized as living in accommodation not 24 hour staffed have a greater level of CMHT contact (r=0.55, p < .001). Similarly, people categorised as living in less-staffed residences were also more likely to be able to go on unaccompanied trips (r=0.36, p < .001).

Cluster 2C involves the two variables relating to friendship, both with people who are nonhandicapped and severely intellectually impaired, and the frequency of doing sporting activities. It will be recalled that all these three variables have been recoded as dichotomous variables. The correlation between the two friendship variables is quite high (r=0.42, p < .001), indicating that people who have no nonhandicapped friends are also likely to have no friends with severe intellectual impairment and vice versa. It is interesting to note that sporting activity is also significantly related to friendship both with people who have severe intellectual impairment (r=0.39, p < .001) and with people who are nonhandicapped, (r=0.26. p < .005). That these two friendship variables relate to sporting activity relatively early in the clustering (level 34) but do not combine with any other variables until much later suggests that sport is a potentially useful way of encouraging friendship. Although the specific correlations between clusters/factors will be discussed later it should be mentioned here that correlations between the friendship variables and other community activities are in the expected direction, i.e. that greater community involvement is associated with greater friendship, and that many of these

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S C A T G S C C P H D N A S A C I N M D A S C

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, 19 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX X XXXXXXX XXXXXXX XXXXXXX

20 XXXXXXXX XXXXXXXXXXXXXXXXXXXXXXXXXXXX X XXXXXXX XXXXXXX XXXXXXX

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22 XXXXXXXX XXXXXXXXXXXXXXXXXXXXXX XXXX X XXXXXXX XXXXXXX XXXXXXX

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29 XXXXX X XXXXXXXXXXXXXXXX XXXX XXXX X XXXXXXX XXXXXXX XXXXXXX

30 XXXXX X XXXXXXXXXXXXXXXX XXXX XXXX X XXXXXXX XXXXXXX XXXXXXX

3 1 XXXXX X XXXXXXXXXXXXXXXX XXXX XXXX X XXXXXXX XXXXXXX X XXXX 32 XXXXX X XXXXXXXXXXXXXXXX XXXX XXXX X X XXXX XXXXXXX X XXXX 3 3 XXXXX X XXXXXXXXXXXXXXXX XXXX XXXX X X XXXX XXXXXXX X XXXX 34 XXXXX X XXXXXXXXXXXXXXXX XXXX X X X X XXXX XXXXXXX X XXXX 35 XXXXX X XXXXXXXXXXXXXXXX XXXX X X X X XXXX XXXX X X XXXX

Figure 2. Cluster 2 shown in larger scale.

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MOSS & HOGG 181

correlations are statistically significant. Cluster 2D is the one containing the age variable. Also involved in this small

cluster is staff input to leisure activities and CMHT/Outreach sample. As mentioned earlier, older people were reported as having greater probability of staff involvement in their additional leisure activities, and were also more likely to be in the Outreach sample than in the CMHT sample.

Clusters 2A - 2D subsequently combine into a single large cluster which was previously described as being related to "community activity". Since age is involved in this large cluster it is worth summarising the significant correlations between this variable and the other variables in the cluster.

Older people:

(a)are more likely to be in the Outreach than in the CMHT sample (p < .0001). (b)have less contact with the CMHT (probably because in this population they are

more likely to be dealt with by the Social Work Teams dealing with the elderly rather than by the CMHT).

(c) are liable to receive fewer individual programmes (p < .05). (d)are less likely to do sport (p < .01) and go shopping (p < .01). (e)are less likely to have a friend who has severe intellectual impairment

(p < .01),

It should be noted that many of the correlations between age and the other variables in this community cluster were not significant. Thus, age was not significantly correlated with:

(a) Residential staffing level. (b)Level of antisocial behaviour. (c) A wide range of community activities including: going to clubs, pubs, cinema,

concerts, seeing sport, going to cafes. (d)Going on trips, either alone or accompanied. (e) Going on holiday in the past five years. (f) Having a friend who is nonhandicapped.

It can be seen, therefore, that there is some evidence that the older people in the population are receiving a lower level of input, specifically in the areas of individual programming, sport and shopping. As described earlier, however, it is apparent that several correlations between age and the other variables in this cluster can be partly accounted for by the finding that the older people were more likely to be identified via the Elderly Social Work Teams than via the CMHT. Age does not, however, cluster with any of the variables relating to mobility, physical medicine etc., nor have our previous analyses shown people in the Outreach sample to have poorer adaptive functioning than the CMHT sample. Thus, there is some indication that the level of people's activity within the community is determined more by the Social Work Team with whom they deal, rather than by their level of physical or functional status. This finding suggests the importance of looking more closely at a comparison of services received by people with severe intellectual impairment from the CMHT and from the Elderly Teams.

Cluster 3, shown in larger scale in Figure 3 incudes two distinct clusters, concerned broadly with,physical medicine (3A) and items relating to Geriatrics/ Mobility (3B). The two variables, Dining Room Aids and Self Injury (ABS2F4) are in the category of variables which are unreliable as population descriptions because

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3A 3B 1 C B G G P D A B G S M T I P W A A D C

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Figure 3. Cluster 3 shown in larger scale.

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MOSS & HOGG 183

very few dining aids were reported and nobody scored highly on self injury. Their apparent association with each other is probably spurious.

Cluster 3A shows the close association between two different kinds of General Practitioner contact (repeat prescriptions and direct treatment) and with treatment for physical medical conditions (variable 15). Chiropody and bathroom aids also feature in this cluster. It is interesting to note that, while foot problems increase markedly with age (Edelstein, 1988) there was no clear association between age and chiropody treatment within this population, Also, the presence of two separate clusters indicates a low correlation between the general treatment of physical conditions encompassed by the variables within this cluster, and the items related to Geriatrics which are found within the second cluster (see below).

Cluster 3B includes all those variables which are associated with poor bodily function and lack of mobility. The variables include most of the types of aids, sensory, mobility, toilet etc, incontinence, physiotherapy, dietician and both of the ABS Part One factors personal/social competence (ABS1FI) and community responsibility (ABS1F2), These last two variables clustered at a very early stage but subsequently joined this "geriatric" cluster, presumably because much of the content of the ABS Part One scale is broadly related to capacity/incapacity. Note also that this is the cluster in which the variable community/hospital occurs. Significant correlations between community/hospital and the other variables in this cluster showed that people living in hospital were more likely to:

(a)be confined to a wheelchair (p < .001) (b)be incontinent (p < .001) (c) have lower scores on personal/community competence (p < .001) (d)were more likely to use a range of aids, including those related to toileting (p <

.0001), dining room (p < .05), seating (p < .0001), mobility (p < .01) and sensory impairment (p < .0001)

(e) were more likely to see the dentist (p < .0001) (f) were more likely to be having attention from a dietician (p < ,0001)

It can be seen quite clearly that members of the population with geriatric problems were more likely to be found in hospital. Note, however, that age does not feature in this cluster, although there is a small correlation in the direction of older people tending to be in hospital (p < .05).

Relations between the clusters

Although it is not posssible to measure directly the degree of association between the clusters, it is possible to achieve this through factor analysis, Elsewhere (Hogg & Moss, 1989) we have shown how prior cluster analysis can make factor analysis more valid by giving information on (a) the appropriateness of factoring the particular data under consideration, and (b) the correct number of factors to extract. In return, factor analysis can give information of the weightings of individual variables within the clusters/factors, and yield individual factor scores which can be used in subsequent analyses. In this particular case, factor scores were used to estimate the degree of association between the clusters. Factor analysis of the variables used Oblimin rotation, which allows for the resulting factors to be correlated. Only those variables in the main clusters (1, 2 and 3 in figure 1) were entered into the analysis. There is no definitive answer to the exact number of factors which are appropriate to extract, From the discussion above it is clear that there are factors corresponding to contact with relatives, community activity,

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community independence, physical treatment and geriatic status. Six factors were specified initially, although seven and eight-factor models were also tried. From the results, however, it was clear that the six-factor model bore the closest relationship to the clustering. Factors seven and eight made no sense either in relation to the clusters, or intuitively. Also, the proport ion of variance accounted for by these factors did not warrant their inclusion. The six-factor model was therefore considered to be the appropriate one.

Resulting variable weightings (see Table 2) confirmed the existence of the six clusters described above. In all, the six factors accounted for 45.7 per cent of the variance, contributions of the specific factors being shown in Table 3. From the correlation matrix between the six factors, shown in Table 4, it can be seen that all the correlations are small, none being statistically significant. Thus, i tcan be concluded that the six clusters are relatively independent of each other.

It will be noticed that "chronological age" does not occur within the list of variables loading on the "geriatric" factor (Factor 1 in Table 2)~ an observation which seemed surprising since the variables in this cluster are obviously age- related. This finding was therefore explored by further analysis.

The fact that age did not cluster with this group was due to the low overall correlation between age and scores on factor 1 (r= 0,13, nonsig.). Throughout the age range under consideration, therefore, age is not a major predictor of the other variables in this cluster. This did not rule out the possibility, however, that high scores on this factor might occur only in the extreme upper end of the age range, rather than being reflected in a gradual increase with increasing age. To test this possibility, members of the populat ion were grouped into four agebands, 50-59, 60-69, 70-79, 80+, and mean factor scores computed for each group.

Mean scores, shown in Figure 4, show that this hypothesis was correct. Scores on

C~er ia t r i c { a c t o r s c o r e 1 .0

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Figure 4. Mean level of"Ger ia t r ic" factor score by ageband.

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MOSS & HOGG 185

"I~ble 2. Variables weighting > 0.4 on each of the six factors (Client variables shown in bold)

FACTOR 1 FACTOR 2 FACTOR 3

Personal/social competence Incontinence Toileting aids Seating aids Mobility aids General aids Physiotherapy Wheelchair use

Withdrawal No. of IPPs received Visits to the pub Cinema going Concert-going Watching sport Visits to cafes Going shopping Accompanied trips Holidays

Chronological age Known/Outreach Community/Hospital CMHT contact Staff input to leisure NH friend contact

FACTOR 4 FACTOR 5 FACTOR 6

Community competence Resid. staffing level Contact with MH friends Contact with NH friends

Contact with relatives? Day visits to relative Receipt of cards

Community/hospital Dental treatment Club attendance Direct GP service Repeat prescriptions Chiropody

Table 3 Percentage of variance accounted for by each cluster/factor

FACTOR PCT OF VAR CUM PCT

1 12.9 12.9 2 10.9 23.8 3 6.4 30.2 4 6.2 36.3 5 5.0 41.3 6 4.4 45.7

Table 4 Factor correlation matrix

FACTOR 1 FACTOR2 FACTOR 3 FACTOR4 FACTOR 5

FACTOR 1 1.00000 FACTOR 2 .13480 1.00000 FACTOR 3 .08314 .06196 1.00000 FACTOR 4 .11260 .09806 .0008 t FACTOR 5 .07864 .07808 .03794 FACTOR 6 .16646 .13832 .00749

No significant correlations

1.00000 .05551 1.00000 .09765 .03124

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the "Geriatric'" factor were dramatically higher in the 80+ group than in any of the other three groups. A comparison of this group with the other three, by one-way analysis of variance, indicated this effect to be significant (p < .025). Also, it can be seen that there is no indication of a trend to higher factor scores across the age groups, which explains the overall lack of correlation with age.

We turn now to a discussion of the interpretation of these factors.

Discussion and Conclusions

The use of cluster analysis on this large and complex data set has demonstrated th e existence of a number of major service and informal support domains within the lives of the population under consideration. The lack of association between the six identified clusters, and the position of client descriptor variables within these clusters, leads to a number of conclusions. In discussing these results we will con- sider factors 1 to 6 (Table 2) in turn.

Factor 1 corresponds to Cluster 3B, the"geriatric" group of variables. The association with personal/social competence is to be expected, while the fact that age does not cluster with this group has been discussed above. Analysis of variance of factor 1 scores, (not presented here), showed that people living in hospital or Part III accommodation for elderly people score higher on this geriatric factor than in other residential categories.

Factor 2 corresponds to cluster 2A~ community activities. The association of these variables with withdrawal is a reflection of the fact that withdrawal is the antithesis of activity. Quite clearly, the clustering of these variables indicates that level of one form of community activity is highly predictive of others. What is particularly noticeable is that neither age nor the ABS Part I factors, personal/social competence and community competence, weights heavily on this factor. Older members of the population did not appear to be particularly disadvantaged with respect to community activity, although there were variations in activity level between Part III accommodation for elderly people and other residential types. Although this form of accommodation only occurred within the Outreach sample, the CMHT/ Outreach dimension did not weight on this factor due to the fact there were several additional types of residential provision in the Outreach group where this disadvan- tage was not evident.

Factor 3 corresponds to cluster 2D, with the addition of the Community/Hospital dimension, This is the factor on which age shows an appreciable weighting, indica- tive of the fact that older people tended to be in hospital or in the Outreach group. Their presence in the Outreach group was a direct result of having been located through social work teams dealing with elderly people. CMHT contact is in this factor since contact /noncontact was the definition of the CMHT and Outreach groups. The association with having a nonhandicapped friend occurs because people located via general services for elderly people tended to live with people who were not severely intellectually impaired,

Factor 4 combines elements of clusters 2B and 2C, with the addition of the functional variable community competence. This factor is the only major departure from the previous clustering, although it makes intuitive sense as it stands: People with high community competence are more likely to live in noncongregate care settings and to have friends, both with and without severe intellectual impairment.

Factor 5 corresponds to cluster I~ contact with relatives. The independence of these variables from all the others confirms the findings of the family interview study on this population (Prosser, 1989). Contact with relatives was independent of a number of factors which might have been likely to demonstrate significant effects:

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MOSS & HOGG 187

There was no difference resulting from (a) client's age, (b) functional level, or (c) category of residence. Thus, it appears that relatives maintain contact with the person largely through a sense of familial obligation and commitment that transcends the characteristics of the person or their location,

Factor 6 corresponds approximately to cluster 3A, the physical condit ions/ treatment cluster, although the factor analysis has also revealed a connection between these variables and the Community/Hospi ta l dimension. Dental treatment, GP services and chiropody are all received to a greater extent by hospital residents. The association with club attendance reflects the finding that club attendance was very" high amongst hospital residents. Indeed. all hospital residents were reported as regularly attending some form of club.

To summarise the patterns of interrelation between client characteristics and the clusters, it was found that Personal/social competence (ABS 1 F 1) was closely related to Cluster 2B, which was labelled "geriatric", while community competence clustered with residential staffing level and friendship patterns. Withdrawal (ABS2F3) was related (negatively) to community activities. Personal maladapta t ion (ABS2F2) and self injury (ABS2F4) were both found to be more or less independent of the major clusters. Antisocial behaviour (ABS2FI) was closely related to the number of IPPs received, although it did not weight highly in the corresponding factor in which IPPs occurred (Factor 2, community activity). Overall, the interactions between the ABS Part Two factors and other variables were not great, as this populat ion did not include many individuals whose problem behaviours were at a severe level.

Sex was also relatively independent of the other variables, although it eventually joined the broad cluster of community activities. Since it is a common observation that women tend to need a higher degree of handicap to be in receipt of support services, the relationships between sex and other variables may not be easily inter- pretable. The only significant correlations between this variable and the others in the cluster were:

(a) mean age of women was greater than men (p < ,05) (b) women were less likely to go on trips alone (p < .01) (c) women were less likely to have a friend with severe intellectual impairment

(p < .05)

This last effect may occur because quite a lot of the women in the populat ion lived in Part I l l accommodat ion for elderly people, where opportunit ies for contact with people with severe intellectual impairment are lower than for contact with nonhandicapped people.

The relation between age and the other variables was a complex one, but most important was the finding that older people tended to be found in general services for elderly people, rather than mental handicap services. The consequences of this, (see Moss et al., 1989 for a full discussion), was a generally poorer level of service in the areas of programming, education and leisure. This CMHT/Outreach service axis was the principal finding in relation to client age and is reflected in the clustering of age with the service axis group (Factor 3), rather than with any specific service variables.

Overall, the use of clustering in this context h as demonstrated the presence of a number of important domains in the service and informal support context of this population. Clustering of client variables within these domains has helped us in our understanding of the complex interaction between the characteristics of individuals and the services they consequently receive,

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188 A CLUSTER ANALYSI S

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Edelstein, J.E. (1988). Foot care for the aging. Physical Therapy. 68, 1882-6. Hogg, J,, & Moss, S, (1987). Survey of people with mental handicap over 50years of age:

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Hogg, J., • Moss, S. (1989 ). A demographic study of older people with mental handicap in Oldham Metropolitan Borough. Manchester: H.A.R.C. University of Manchester.

Hogg, J., Moss, S., & Cooke, D. (1988), Ageing and mental handicap, London: Chap- man and Hall.

Horne, M. (1989).A demographic study of older people with mental handicap in Oldham Metropolitan Borough, Part 4, Manchester: H.A.R.C., University of Manchester.

Janicki, M.P., & Wisniewski, H.M. (Eds.) (1985).Aging and developmental disabilities: Issues and approaches. Baltimore: Paul Brookes Publishing.

Janicki, M.P, & MacEachron, A.E. (1984). Residential, health, and social service needs of elderly developmentally disabled persons. The Gerontologist, 24, 128- 137

Moss, S,, Hogg, J., & Home, M. (1989).A demographic study of older people w#h mental handicap in Oldham Metropolitan Borough, Part 2, Manchester: H.A.R.C. Univer- sity of Manchester.

Nihira, K. (1976). Dimensions of adaptive behavior in institutionalized mentally retarded children and adults: Developmental perspective. American Journal of Mental Deficiency, 81. 215-226

Prosser, H (1989).A demographic study of older people with mental handicap in Oldham Metropolitan Borough, Part 3, Manchester: H.A.R,C., University of Manchester.

Seltzer, M.M, & Krauss, M.W. (1987). Aging and mental retardation: Extending the continuum, Washington, DC: American Association on Mental Deficiency.

This publication is available in microform from University Microfilms International. Call toll-free 800-521-3044, ha Mich] Alaska and Hawai i call coUecl 313-7 marl inqmry to: Umversity I'vhcrofiIrr 300 North Zeeb Road, Ann Arbor. Iv

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Documents

A cluster analysis of support networks of older people with severe intellectual impairment