SK.
SCL
&
Med.,
Vol.
9. pp.
257
lo
262
Pergamon
Press 1975. Printed
m Great
Britam
A PATH ANALYTIC MODEL OF PSYCHIATRIC HOSPITAL PERFORMANCE R. E. A. MAPES and M. J. CLARKE Centre for Social Science Research, University of Keele Abstract-This paper presents the results of the analysis of annual returns (SBH 112) frbm most Psychiatric Hospitals to the Department of Health. These and other data have been analysed in such a way as to throw some light on the wide variations in performance which can be detected between individual Psychiatric Hospitals. It is clear that one is not entitled to speak’ of “success” or “failure” since the available data on
re-admission of in-patients, and data on out-patients’ performance is so limited. However, the study throws some light on the differing strategies which exist for dealing with all but severe psychiatric illness. The path analysis explores the relationship between these alternative strategies and indicates the association between expenditure on the Hospital Service, Staffing Levels and Discharge Rate in a precise manner.
This paper presents the results of a statistical causal (path) analysis carried out on selected official data relating to all of the Mental Illness Hospitals in England and Wales. The basic object of the operation was to identify and quantify significant determinants of the varying performance of these hospitals, as measured on certain selected indices which will be explained later. The operation involved the analysis of a large amount of data on a National Scale. Before we describe this analysis, it is perhaps pertinent to say a word or two in justification of such a global approach. A stage is reached in the research of any broadly administrative area when further particularistic research adds only fractionally to a complete understanding of the process at work or may even confuse rather than clarify. Every now and then it is necessary to halt and take stock. This point has been stressed by the carrying out of two excellent welfare studies [l. 23 which have collected together the results of large numbers of valuable small studies and have embodied in a structural summary the variables which these studies showed to be important. This point was also made some years ago in connection with the problem of perinatal loss, in which field it is possible to carry out countless researches on, say, admission procedures alone. A comprehensive study [3] proved an indicator of the most important variables on a national scale. No claim is made that such an approach can ever be the last word, but such designs. which take most of the strategic variables together. rather than two at a time, are useful as a means of gauging progress. Indeed, such summaries inevitably result in implications for further research of the particularistic type and can show how relevant such further small-scale researches might be in the overall context. There are. of course, difficulties and limitations for the summarizing structural approach. Inevitably the data demands are great and so considerable dependence usually has to be placed on offi* The use of statistical path analysis is now quite widespread. An elementary exposition is given in Moser and Kalton (1971). More advanced and recent departures are listed in Mapes and Allan (1973) [6. 73.
cial sources. This can have drawbacks since such data are sometimes collected with little more rationale than to meet the needs of administrative tidiness. In such cases the data do not always appear in such forms as permit the study of effects indicated as important in particularistic studies. These drawbacks do not, however, negate the value of doing some overall quantitative analysis of this kind in the medical field. Both Kalton and Susser [4,5], fdr example, have recently espoused the value of the large quantitative approach whilst, as already mentioned, Davies has produced valuable studies in a related field based on the pathanalytic approach.* Let us now look at the relevance of such an approach at this time in the field of Psychiatric Hospital performance. AIMS OF THE PSYCHIATRIC HOSPITAL AND SOME METHODOLOGKAL IMPLICATIONS OF TESTING THESE AIMS The analysis of organizations has traditionally stressed aims [8-lo]. Recently, Robinson [ll], referring to hospital organization, has taken Etzioni [12] and others to task for applying the business organization model too rigorously to other organizational entities. Although in the work referred to, Etzioni clearly shows that he appreciates the existence of multiple alternative aims and he specifically cites the mental hospital case, Robinson’s point is salutory. One is clearly entitled to ask “whose aims’?’ For example. there are at least three objectives of a psychiatric hospital, viz.: (a) care-the maintenance of life, health, comfort and morale of the patient; (b) protection-of the public and patient; and (c) social restoration-of patients to their usual role in the community. It is clear that not only do these goals not amount to the same thing but they may, in fact, often conflict with each other. A number of measures df achieved aim do exist and provide highly scaled numerical data, for example, cost per case [ 133 or Ullmann’s First Significant Release [14] which is an attractive composite of therapeutic achievement and the efficiency of supportive services. However, as already stated, our study is large scale and neither the official Patients’ Census
R. E. A. MAPES and M. J. CLARKE
258
nor the Mental Health Inquiry provide data comparable in detail with Ullmann’s. This problem refocusses the initial dilemma, i.e. while it is possible to derive high order data for local studies, they are rarely representative. The task of collecting comprehensive area or national data is, however, often extremely complex and such highly relevant data are not at present available for every hospital. We have, however, some adequate reflectors of achievement which will be termed performance indices. Within any psychiatric treatment system there is considerable discretion as to the organizational approach used to achieve the medical and social objectives of the institution. Each organizational approach may be characterized by a number of these performance indices. The use of differing strategies, as reflected by differing results on the indices, is itself reflected and influenced, we suggest, by a number of what we term predictor variables. For example it is somewhat unrealistic to have a treatment approach orientated towards out- and daypatient treatment if the hospital is very inaccessible. For this reason one may regard “accessibility” as a likely predictor of the performance variables. Or otherwise. it could be viewed as unwise to strive for ever-decreasing in-patient length of stay if the catchment area suffers a severe shortage of social workers. Accordingly, number of social workers per unit catchment population is also deserving of inclusion as a possible explanatory variable. A cursory look at the National Variations in some of these indices of performance reveals that there is wide variation which may well be amenable to explanation on a National Scale but overlooked in particularistic research. For example, see Tables 1 and 2. The paper demonstrates how both the performance indices and their candidate predictor variables were reduced to manageable numbers. We then explore the ways in which both sets of variables are structurally interrelated. The degree of dependence of each variable on each of the others will be noted and finally the implications of this degree of dependence will be discussed.
HOSPITALS AND DATA
The major source of data for this analysis was the SBH 112 form which is returned annually by each psychiatric hospital. SBH 112 contains details of staff, patient turnover, bed occupancy, castings, etc. Since we wished to ascertain the constancy of our models over time it was necessary to carry out the analyses on a hospital sample which was not affected by the passage of time, by minor re-organization, change in DHSS definition of a psychiatric hospital, etc. This meant that 99 non-teaching hospitals’ data were to hand over a 3-yr period.* For such a complex problem in structural function, 99 hospitals was a small sample, particularly bearing in mind that the prelirninary list of predictor variables consisted of 47 variables and that data were also collected for 13 performance indices. A full list of the variables are given as an annexe to this paper. * One hundred and sixteen hospitals (or small “groups” of hospitals) are listed as having 200 or more beds in 1970.
Table
1. Annual
admission rates per population (PeZ)
1000 catchment
Number of hospitals
1971
I
1.9 2+ 3G 4.0506.tF6.3
23 w 21 7 , Total 99
National average 3.61. Table
2. New
out-patients per annum catchment population (Pe6)
1971
per
100.000
Number of hospitals 7 28 32 21 3 1 1 Total 99
10% 2W 3W 4cO500600700-701
.
National average 382 [IS].
AN&Y SIS
The 47 predictor variables could be broadly grouped as follows: (1) environmental; (2) professional; (3) institutional; and (4) socio-medical. Briefly, the Environmental group related to the effects on the hospital of its location with respect to its immediate environment. The Professional group summarized the professional and clinical facilities available at the hospital. The Institutional group related to the hospital itself as an organization; thus note was taken of visiting policy, ward size, proportion of patients working, etc. The Socio-medical group summarized sociodemographic characteristics of the catchment population which may be relevant to the epidemiology of the conditions which we are studying. Clearly this last group of variables is totally outside the control of the hospital itself. In order to reduce the total set of variables to manageable proportions a series of multivariate approaches were employed. The object of this process was to isolate the important variables by excluding those which showed little significant associations with other variables or which were so closely associated with another variable that they were measuring very similar things; a clear example of this being the Accessibility Indices. In the first place a number of variables were excluded as a result of conventional partial correlation examination. Following this. principal components analysis within each predictor group led to further reductions in numbers of variables. Finally, since the analytical schema provided such a classically appropriate case [ 161, canonical correlation analysis between the predictor and performance variables was carried out and this led to final reduction in the predictor groups as well as within the group of performance indices. We were therefore left with a much
A model of psychiatric hospital performance
smaller group of variables on which the path analysis was performed. Where the dimensionality of variables was in doubt. factor scores were sometimes used in place of original data [7]. These steps were adopted in the interests of simplification and validity of effect. The list of variables which showed significant paths is given below. excluding those significant paths to variables which themselves did nothing to explain the variability of the performance indices (see Appendix). Pel No. of in-patients per U.C.P. (Unit Catchment
Index of hospital inaccessibility Overall Professional and Nursinr! Staff level Overall in-patient expenditure V Size.
In the above list the performance from the predictor type.
variables are separated
In the reduced set of variables, four only of the performance indices are still retained. Of the predictor variables. by far the most important are the professional ones connected with basic staffing and in-patient treatment costs. It is worth commenting here on the absence from the final list of an index of admission rates (mentioned earlier). From this analysis the index Pe2, admission rate per Unit Catchment Population, appears to be composed of two basic elements. A high admission rate will clearly be caused by a large number of inpatients per U.C.P. (Pel). But a hospital with higher patient turnover and discharge rates is also likely to have a more frequent re-admissions of patients which would also increase its Pe2 values. Therefore clarification is aided by the exclusion of Pe2 and the inclusion of Pel and Pe5 which are strongly negatively correlated. An admission rate measured per hospital bed is, as would be expected, highly correlated with the Discharge Rate which is included in the final path analysis. Let us now consider the implications of the sign and size of the path coefficients, which are represented diagrammatically (Fig. 1). The central importance of three variables, namely Cl the overall costing level, Pl the overall staffing level and Pe5 the discharge rate, in the explanation of the process is immediately apparent. It is, of course, appropriate that an examination of the work of a psychiatric hospital should have the patient discharge rate as one of its key dependent variables and in this case it is the performance variable with the most variance accounted for. We see a very strong path from Pl to Pe5 indicating a strong positive effect on the Discharge rate produced by a rise in the staffing level. The index of staffing level is a measure of overall staffing level derived from factor scores obtained from an earlier analysis of several highly correlated staffing indices, and is of course a ratio measured per in-patient. It is quite clear that a favourable staffing ratio has a pronounced effect of raising the patient discharge rate. This directly confirms the findings of an American study. the Psychiatric Evaluation Project based on a cohort of patients admitted to 12 hospitals: it
EIB
S
Population) Pe5 Discharge rate Pe8 Total no. of out-patient attendances per annum per U.C.P. Pe9 Total no. of day-patient attendances per annum per U.C.P. El b PI Cl S
259
pe,
_
-052
v
Pe5
a,
‘\Pe9
Fig. 1
also confirms the work of [13]. whose work was, however, based only on three hospitals. Ullmann also found a significant positive correlation between his measures of effectiveness and high staff/patient ratios Cl43
As was the case with Pl, the overall costing index Cl had been derived from factor scores on several costing indices relating to expenditure in individual hospital departments. Stafhng costs are, of course, an element in this and it is to be expected therefore that there is a positive relationship between the level of expenditure per patient and the staffing level. It may well also be the case that expenditure commitment in other areas requires an increase in Staffing commitment. The direct effect of Cl on Pe5 is however a negative one. It is smaller than the positive indirect effect via the staffing level but is, nevertheless, surprising at first sight. It would appear that an increase in total expenditure produces a higher staffing level and, in this way, produces a corresponding increase in the discharge rate. The other costing elements, however, produce a countervailing direct negative effect on the discharge rate. That is, an increase in those costing components lowers the discharge rate. The fact that this direct effect is less than the indirect effect via the staffing ratios reflects the strengths of the relationships between the costing and staffing variables and between the staffing variable and the discharge rate. This is an excellent example of how the path analysis technique may throw light on the real process at work, since a simple correlation between Cl and Pe5 is positive because of the strength of the indirect effects via the staffing ratio. The direct negative effect which the other costing elements have on the Discharge rate is worthy of comment. We have previously found that there is a correlation between the total expenditure and the expenditure in individual sections of the budget. This confirms findings by Moores and Casmas [17] with respect to subnormality hospitals. Thus this negative effect is not merely a function of the expenditure on staffing with less resources being devoted to other’ purposes instead of staffing. It is a real negative effect and compares with the findings of Ullmann who noted that “the amount of money spent on food and shelter tended to be negatively associated with early
260
R. E. A. MAPES and M. J. CLARKE
release”. We do not at present have data relating to individual fengths of stay and therefore, a direct comparison with his findings is not possible. However, it seems highly probable that a high discharge rate reflects a high “early release” rate and we therefore may have some co~rmation of Ull~nn’s suggestion that certain types of administrative expenditure operate against rapid .discharge. There are a number of interpretations of this finding. For instance high expenditure of this kind might refiect a low involvement of patients taking a working roie in hospital maintenance. Such a working role is commonly assumed to have a beneficial effect. Aiternatively, high expenditure on some elements may even tend to retain those patients who wish to postpone release. However, a number of other equally beguiling explanations do exist. Suffice to say there is a substantial direct relationship which shows that the more generally spent on patients---the lower their discharge rate and this militates against the much stronger specific positive effect of higher expenditure on clinical and nursing staff producing the desired effect on discharge. It will also be seen that the actual size of the hospital has a negative effect on Cl, the overall cost index. That is to say, the cost per patient is higher in smaller hospitals. This might well be anticipated on general economy of scale principles but this analysis highlights the perhaps “hidden” indireet effects which the size of a hospital may have on its performance. The overall level of statig also has strong direct positive effects on two other performanct? variables, that is, Fe8 and Pe9, the annual number of out- and day-patient attendances (per Unit Catchment Population) respectively. We have already seen that the overall level of costing and indeed the hospital size will also, therefore, have indirect positive and negative effects respectively on these two perfurmanoe indices. Since, as we have already seen, the staffing level has a positive direct effect on the Discharge Rate, it is not surprising that it should show a similar direct effect on the out- and &y-patient indices since an increase in the level of out- and day-patient activity at a hospital is another aspect of the same policy which produces a high discharge rate. It represents an emphasis on day- and out-patient care rather than in-patient care. This is why we also see a strong negative association between the Discharge Rate and Pel, the number of In-patients per Unit Catchment Population. Clearly, a rapid discharge rate will cause a run-down in the number of permanent In-patients. (For comments on the playing of Pel, Pe8 and Pe9 against each other, see [ 18‘J.) We notice also that the index of inaccessibility of the hospital has a negative effect on the performance index Pe9, the number of day-patient at~n~n~s. This is quite clearly what one would expect on prima facie grounds but it is interesting to see such clear associations emerging and would seem to support the view advanced by Hoenig and Wamilton that the location of the ho~i~ in refation to the ~pulation it is serving can be an extremely important determinant of the way in ,which that hospital functions [193. This is, of course, one of the principal arguments for the establishment of psychiatric units in General Hospitals which are generally more accessible to the Catehment Papulation which they serve than is the
ease with the ~jority hospitals.
of the traditional
mental illness
CONCLUSION
At the end of the day we have a situation where a large number of candidate explainer variables and performance indices have been reduced to a very small number indeed; this residue having then been used in a representation of the causal process. This does not mean that all of the variables which do not appear in the picture are irrelevant as determinants or measures of the way in which a hospital functions. There is ample scope for further research into the effects of some of these variables. We have, in this p~~n~tion, attemp~d to err on the side of simplicity since one of the valid criticisms of path analysis studies at the present is a tendency to over-complexity. What we do claim, however, is that we have isolated, using national data, the crucial variables with respect to a determinant influence on the performance indices as we have chosen them. We have strong statistical evidence of the importance of the relationships between expenditure levels, staffing Ievel, discharge rates and the number of in-~ti~nt~ per unit catchment popuiation. We believe that this in it&f is justification for this approach. We have tried to explore and explain these relationships and this may give rise to further, more partieularistic, research in this area. What is paramount is the very strong relationship between the level of trained staff and the Pe measures of activity and achievement. In the balance the indieation would seem to be a con~n~ation on more staff per hospital rather than more beds. Nevertheless, it is quite clear that until precise data on re-admission is available one cannot really be certain of such conclusions. Further partieularistic research is. of course, essential for a full understanding of the processes at work. We appreciate that measures such as discharge and re-admission rate are in themselves rather inadequate measures of success. Indeed there will be disagreement about how far, for example, frequent discharge and re-admission, as opposed to continued in-patient treatment, constitutes “sueeess” for the individual patient, What perhaps is ultimately required is Further work on some measure of social competence before admission and after discharge. However, such data do not, at present, exist and. indeed, it is difficult to see how much data could be available on a National Scale. We have been concerned to examine the variations in the national data which is available to obtain an indication of the relationships which exist between such factors as stafing levels and the discharge rate. This will, we hope, assist in the selection of areas for detailed parti&ularistic research and also indicates the probable consequences of policy decisions relating to staffing ievels and other similar factors, without committing ourselves as to whether particular consequences are good or bad. REFERENCES
1.
Davies B. P. et al. Vuriatfons in Srrvic~s for the Aged. B&f. London. 1970. -^.--- G.
261
A model of psychiatric hospital performance 3. Mapes R. E. A. and Heywood R. T. The analysis of regional variation in infant and perinatal mortality rates. Oxford University Press for Nuffield Provincial Hospitals Trust, London, 1970. 4. Kalton G. The role of population surveys as a source of morbidity and other health data. Statistician 21, 301, 1972. 5. Susser M. Causal Thinking in the Health Sciences. Oxford University Press, Oxford, 1973. 6. Moser C. A. and Kalton G. Survey Methods in Social Investigations. Heinemann, London, 1971. 7. Mapes R. E. A. and Allan G. J. B. Path analysis: a cautionary note. Sot. Rev. 21, 137, 1973. 8. Parsons T. Suggestions for a sociological approach to the theory of organization. Admin. Sci. Q. 1, 63, 1956. 9. Blau P. M. and Scott W. R. Formal Organizations. Chandler, San Francisco. 1962. 10. Haas J. E. and Drabek T. E. Complex Organizations: A Sociological Perspective. Macmillan, New York, 1973.
APPENDIX:
SUMMARY
LISTS OF INDICES
11. Robinson D. Patients, Practitioners and Medical Care. Heinemann, London, 1973. 12. Etzioni A. Modern Organizations. Prentice-Hall, Englewood Cliffs, New Jersey, 1964. 13. Jones K. and Sidebotham R. Menta/ Hospitals at Work. Routledge & Kegan Paul, London, 1962. 14. Ullmann L. P. Institution and Outcome. Pergamon Press, Oxford, 1967. 15. D.H.S.S. The Facilities and Services of Psychiatric Hospitals 1970. Statistical Research Report Series No. 2, HMSO, 1972. 16. Van de Geer J. P. Introduction to Multioariate Analysis for the Social Sciences. W. H. Freeman, San Francisco, 1971. 17. Moores B. and Casmas S. (n.d.). Unpublished paper. 18. Mapes R. E. A. and Wilson B. J. My In press, 1975. 19. Hoenia J. and Hamilton Marian W. The De-seareaation of-the Mentally Ill. Routledge & Kegan Paul, L&tdon, 1969.
OF PERFORMANCE
AND
EXPLAINER
VARIABLES
Indices of L‘performance”
Pel Pe2 Pe3
No. of in-patients
per 1000 catchment population. No. of annual admissions per loo0 catchment population. Patients’ turnover rate =
admissions + discharges + deaths 2 x average availablestatfed
Pe4
beds
(all per annum).
Death rate annual deaths x loo. = 365 x daily average resident patients
Pe5 Pe5a
Discharge rates. Basic discharge rate annual discharges x 100. = 365 x daily average of resident patients
Pe5b
annual short-term patient discharges x loo. = 365 x daily average of short-term resident patients
PeSc
annual non-elderly patient discharges x loo. = 365 x daily average of non-elderly resident patients
Pe6 Pe7 Pe8 Pe9 PelO Pell
No. of new out-patients per annum No. of new day-patients per annum Total no. of out-patient attendances Total no. of day-patient attendances No. of out-patient attendances per No. of day-patient attendances per
per 1000 catchment population. per 1000 catchment population. per annum per 1000 catchment population. per annum per 1000 catchment population. 100 in-patient days. 100 in-patient days. Explainer
variables
Environmental
Ela-c
Indices of hospital accessibility from its catchment population.
Indices of local authority community services No. of welfare service social workers per loo0 catchment population. No. of mental health social workers per 1000 catchment population. No. of places per 1000 catchment population for the mentally ill provided by the local authority in workshops or day centres. No. of junior places per 1000 catchment population available in local authority training centres (including E2d special units). As E2d but for adult places. E2e
E2a E2b E2c
Indices of co-ordination between the hospital and the local authority Official co-ordination. E3a Actual contact. E3b Index of the extent of involvement of voluntary organizations. E4
R. E. A. MAPESand M. J. CLARKE
262 Professional
Prla Prlb Prla Pr2b Pr3a Pr3b Pr3c Pr3d Pr4
(number per 100 in-patients in each case) Consultant psychiatrists. Other psychiatric medical staff. Trained nurses. Other nurses. Psychologists. Psychiatric social workers. Therapists. Instructors/teaching staff. Domestic assistants and ward orderlies.
Ii7stitutional
Ila Ilb I2 I3 14 15a 15b I6
“6 of resident patients working in domestic and hospital service departments. 7< of resident patients working elsewhere, i.e. handicrafts. etc. 7; of patients in wards of 50 or more. Average y0 bed occupancy. Number of years elapsed since qualification of medical superintendent. y;, of beds with a space of less than 50 sq. ft. O’ of beds with a space of more than 60 sq. ft. Gisiting policy.
Institutional-costing
In-patients ICl IC2 IC3a IC3b IC3c ICM IC4
Cost of drugs. Total ward cost. Cost of pathology. Cost of pharmacy. Cost of ancilliary medicine. Medical service departments . Total in-patient cost.
Out-patients Cost ICOl ICO2 Total IC03 Cost ICO4 Total IC05
=
cost.
of drugs (equivalent to ICl). out-patient departments cost (equivalent to IC2). of treatment departments only (equivalent to IC3d). out-patient cost (equivalent to IC4).
Net total out-patient cost (IC04) total in-patient cost (IC4)
Socio. .medical SI
S2a S2b S3a S3b s3c s4 s5 S6
% of % of % of “//,of % of y/, of % of % of % of
catchment population who are male. male catchment population, over 15, who are married. female catchment population, over 15, who are married. male catchment population in executive and professional classes. male catchment population in other non-manual and skilled manual classes. male catchment population in unskilled and semi-skilled classes. admitted patients who are informal patients. admitted patients who are aged 65 or over. admitted patients who are male.
SCL
&
Med.,
Vol.
9. pp.
257
lo
262
Pergamon
Press 1975. Printed
m Great
Britam
A PATH ANALYTIC MODEL OF PSYCHIATRIC HOSPITAL PERFORMANCE R. E. A. MAPES and M. J. CLARKE Centre for Social Science Research, University of Keele Abstract-This paper presents the results of the analysis of annual returns (SBH 112) frbm most Psychiatric Hospitals to the Department of Health. These and other data have been analysed in such a way as to throw some light on the wide variations in performance which can be detected between individual Psychiatric Hospitals. It is clear that one is not entitled to speak’ of “success” or “failure” since the available data on
re-admission of in-patients, and data on out-patients’ performance is so limited. However, the study throws some light on the differing strategies which exist for dealing with all but severe psychiatric illness. The path analysis explores the relationship between these alternative strategies and indicates the association between expenditure on the Hospital Service, Staffing Levels and Discharge Rate in a precise manner.
This paper presents the results of a statistical causal (path) analysis carried out on selected official data relating to all of the Mental Illness Hospitals in England and Wales. The basic object of the operation was to identify and quantify significant determinants of the varying performance of these hospitals, as measured on certain selected indices which will be explained later. The operation involved the analysis of a large amount of data on a National Scale. Before we describe this analysis, it is perhaps pertinent to say a word or two in justification of such a global approach. A stage is reached in the research of any broadly administrative area when further particularistic research adds only fractionally to a complete understanding of the process at work or may even confuse rather than clarify. Every now and then it is necessary to halt and take stock. This point has been stressed by the carrying out of two excellent welfare studies [l. 23 which have collected together the results of large numbers of valuable small studies and have embodied in a structural summary the variables which these studies showed to be important. This point was also made some years ago in connection with the problem of perinatal loss, in which field it is possible to carry out countless researches on, say, admission procedures alone. A comprehensive study [3] proved an indicator of the most important variables on a national scale. No claim is made that such an approach can ever be the last word, but such designs. which take most of the strategic variables together. rather than two at a time, are useful as a means of gauging progress. Indeed, such summaries inevitably result in implications for further research of the particularistic type and can show how relevant such further small-scale researches might be in the overall context. There are. of course, difficulties and limitations for the summarizing structural approach. Inevitably the data demands are great and so considerable dependence usually has to be placed on offi* The use of statistical path analysis is now quite widespread. An elementary exposition is given in Moser and Kalton (1971). More advanced and recent departures are listed in Mapes and Allan (1973) [6. 73.
cial sources. This can have drawbacks since such data are sometimes collected with little more rationale than to meet the needs of administrative tidiness. In such cases the data do not always appear in such forms as permit the study of effects indicated as important in particularistic studies. These drawbacks do not, however, negate the value of doing some overall quantitative analysis of this kind in the medical field. Both Kalton and Susser [4,5], fdr example, have recently espoused the value of the large quantitative approach whilst, as already mentioned, Davies has produced valuable studies in a related field based on the pathanalytic approach.* Let us now look at the relevance of such an approach at this time in the field of Psychiatric Hospital performance. AIMS OF THE PSYCHIATRIC HOSPITAL AND SOME METHODOLOGKAL IMPLICATIONS OF TESTING THESE AIMS The analysis of organizations has traditionally stressed aims [8-lo]. Recently, Robinson [ll], referring to hospital organization, has taken Etzioni [12] and others to task for applying the business organization model too rigorously to other organizational entities. Although in the work referred to, Etzioni clearly shows that he appreciates the existence of multiple alternative aims and he specifically cites the mental hospital case, Robinson’s point is salutory. One is clearly entitled to ask “whose aims’?’ For example. there are at least three objectives of a psychiatric hospital, viz.: (a) care-the maintenance of life, health, comfort and morale of the patient; (b) protection-of the public and patient; and (c) social restoration-of patients to their usual role in the community. It is clear that not only do these goals not amount to the same thing but they may, in fact, often conflict with each other. A number of measures df achieved aim do exist and provide highly scaled numerical data, for example, cost per case [ 133 or Ullmann’s First Significant Release [14] which is an attractive composite of therapeutic achievement and the efficiency of supportive services. However, as already stated, our study is large scale and neither the official Patients’ Census
R. E. A. MAPES and M. J. CLARKE
258
nor the Mental Health Inquiry provide data comparable in detail with Ullmann’s. This problem refocusses the initial dilemma, i.e. while it is possible to derive high order data for local studies, they are rarely representative. The task of collecting comprehensive area or national data is, however, often extremely complex and such highly relevant data are not at present available for every hospital. We have, however, some adequate reflectors of achievement which will be termed performance indices. Within any psychiatric treatment system there is considerable discretion as to the organizational approach used to achieve the medical and social objectives of the institution. Each organizational approach may be characterized by a number of these performance indices. The use of differing strategies, as reflected by differing results on the indices, is itself reflected and influenced, we suggest, by a number of what we term predictor variables. For example it is somewhat unrealistic to have a treatment approach orientated towards out- and daypatient treatment if the hospital is very inaccessible. For this reason one may regard “accessibility” as a likely predictor of the performance variables. Or otherwise. it could be viewed as unwise to strive for ever-decreasing in-patient length of stay if the catchment area suffers a severe shortage of social workers. Accordingly, number of social workers per unit catchment population is also deserving of inclusion as a possible explanatory variable. A cursory look at the National Variations in some of these indices of performance reveals that there is wide variation which may well be amenable to explanation on a National Scale but overlooked in particularistic research. For example, see Tables 1 and 2. The paper demonstrates how both the performance indices and their candidate predictor variables were reduced to manageable numbers. We then explore the ways in which both sets of variables are structurally interrelated. The degree of dependence of each variable on each of the others will be noted and finally the implications of this degree of dependence will be discussed.
HOSPITALS AND DATA
The major source of data for this analysis was the SBH 112 form which is returned annually by each psychiatric hospital. SBH 112 contains details of staff, patient turnover, bed occupancy, castings, etc. Since we wished to ascertain the constancy of our models over time it was necessary to carry out the analyses on a hospital sample which was not affected by the passage of time, by minor re-organization, change in DHSS definition of a psychiatric hospital, etc. This meant that 99 non-teaching hospitals’ data were to hand over a 3-yr period.* For such a complex problem in structural function, 99 hospitals was a small sample, particularly bearing in mind that the prelirninary list of predictor variables consisted of 47 variables and that data were also collected for 13 performance indices. A full list of the variables are given as an annexe to this paper. * One hundred and sixteen hospitals (or small “groups” of hospitals) are listed as having 200 or more beds in 1970.
Table
1. Annual
admission rates per population (PeZ)
1000 catchment
Number of hospitals
1971
I
1.9 2+ 3G 4.0506.tF6.3
23 w 21 7 , Total 99
National average 3.61. Table
2. New
out-patients per annum catchment population (Pe6)
1971
per
100.000
Number of hospitals 7 28 32 21 3 1 1 Total 99
10% 2W 3W 4cO500600700-701
.
National average 382 [IS].
AN&Y SIS
The 47 predictor variables could be broadly grouped as follows: (1) environmental; (2) professional; (3) institutional; and (4) socio-medical. Briefly, the Environmental group related to the effects on the hospital of its location with respect to its immediate environment. The Professional group summarized the professional and clinical facilities available at the hospital. The Institutional group related to the hospital itself as an organization; thus note was taken of visiting policy, ward size, proportion of patients working, etc. The Socio-medical group summarized sociodemographic characteristics of the catchment population which may be relevant to the epidemiology of the conditions which we are studying. Clearly this last group of variables is totally outside the control of the hospital itself. In order to reduce the total set of variables to manageable proportions a series of multivariate approaches were employed. The object of this process was to isolate the important variables by excluding those which showed little significant associations with other variables or which were so closely associated with another variable that they were measuring very similar things; a clear example of this being the Accessibility Indices. In the first place a number of variables were excluded as a result of conventional partial correlation examination. Following this. principal components analysis within each predictor group led to further reductions in numbers of variables. Finally, since the analytical schema provided such a classically appropriate case [ 161, canonical correlation analysis between the predictor and performance variables was carried out and this led to final reduction in the predictor groups as well as within the group of performance indices. We were therefore left with a much
A model of psychiatric hospital performance
smaller group of variables on which the path analysis was performed. Where the dimensionality of variables was in doubt. factor scores were sometimes used in place of original data [7]. These steps were adopted in the interests of simplification and validity of effect. The list of variables which showed significant paths is given below. excluding those significant paths to variables which themselves did nothing to explain the variability of the performance indices (see Appendix). Pel No. of in-patients per U.C.P. (Unit Catchment
Index of hospital inaccessibility Overall Professional and Nursinr! Staff level Overall in-patient expenditure V Size.
In the above list the performance from the predictor type.
variables are separated
In the reduced set of variables, four only of the performance indices are still retained. Of the predictor variables. by far the most important are the professional ones connected with basic staffing and in-patient treatment costs. It is worth commenting here on the absence from the final list of an index of admission rates (mentioned earlier). From this analysis the index Pe2, admission rate per Unit Catchment Population, appears to be composed of two basic elements. A high admission rate will clearly be caused by a large number of inpatients per U.C.P. (Pel). But a hospital with higher patient turnover and discharge rates is also likely to have a more frequent re-admissions of patients which would also increase its Pe2 values. Therefore clarification is aided by the exclusion of Pe2 and the inclusion of Pel and Pe5 which are strongly negatively correlated. An admission rate measured per hospital bed is, as would be expected, highly correlated with the Discharge Rate which is included in the final path analysis. Let us now consider the implications of the sign and size of the path coefficients, which are represented diagrammatically (Fig. 1). The central importance of three variables, namely Cl the overall costing level, Pl the overall staffing level and Pe5 the discharge rate, in the explanation of the process is immediately apparent. It is, of course, appropriate that an examination of the work of a psychiatric hospital should have the patient discharge rate as one of its key dependent variables and in this case it is the performance variable with the most variance accounted for. We see a very strong path from Pl to Pe5 indicating a strong positive effect on the Discharge rate produced by a rise in the staffing level. The index of staffing level is a measure of overall staffing level derived from factor scores obtained from an earlier analysis of several highly correlated staffing indices, and is of course a ratio measured per in-patient. It is quite clear that a favourable staffing ratio has a pronounced effect of raising the patient discharge rate. This directly confirms the findings of an American study. the Psychiatric Evaluation Project based on a cohort of patients admitted to 12 hospitals: it
EIB
S
Population) Pe5 Discharge rate Pe8 Total no. of out-patient attendances per annum per U.C.P. Pe9 Total no. of day-patient attendances per annum per U.C.P. El b PI Cl S
259
pe,
_
-052
v
Pe5
a,
‘\Pe9
Fig. 1
also confirms the work of [13]. whose work was, however, based only on three hospitals. Ullmann also found a significant positive correlation between his measures of effectiveness and high staff/patient ratios Cl43
As was the case with Pl, the overall costing index Cl had been derived from factor scores on several costing indices relating to expenditure in individual hospital departments. Stafhng costs are, of course, an element in this and it is to be expected therefore that there is a positive relationship between the level of expenditure per patient and the staffing level. It may well also be the case that expenditure commitment in other areas requires an increase in Staffing commitment. The direct effect of Cl on Pe5 is however a negative one. It is smaller than the positive indirect effect via the staffing level but is, nevertheless, surprising at first sight. It would appear that an increase in total expenditure produces a higher staffing level and, in this way, produces a corresponding increase in the discharge rate. The other costing elements, however, produce a countervailing direct negative effect on the discharge rate. That is, an increase in those costing components lowers the discharge rate. The fact that this direct effect is less than the indirect effect via the staffing ratios reflects the strengths of the relationships between the costing and staffing variables and between the staffing variable and the discharge rate. This is an excellent example of how the path analysis technique may throw light on the real process at work, since a simple correlation between Cl and Pe5 is positive because of the strength of the indirect effects via the staffing ratio. The direct negative effect which the other costing elements have on the Discharge rate is worthy of comment. We have previously found that there is a correlation between the total expenditure and the expenditure in individual sections of the budget. This confirms findings by Moores and Casmas [17] with respect to subnormality hospitals. Thus this negative effect is not merely a function of the expenditure on staffing with less resources being devoted to other’ purposes instead of staffing. It is a real negative effect and compares with the findings of Ullmann who noted that “the amount of money spent on food and shelter tended to be negatively associated with early
260
R. E. A. MAPES and M. J. CLARKE
release”. We do not at present have data relating to individual fengths of stay and therefore, a direct comparison with his findings is not possible. However, it seems highly probable that a high discharge rate reflects a high “early release” rate and we therefore may have some co~rmation of Ull~nn’s suggestion that certain types of administrative expenditure operate against rapid .discharge. There are a number of interpretations of this finding. For instance high expenditure of this kind might refiect a low involvement of patients taking a working roie in hospital maintenance. Such a working role is commonly assumed to have a beneficial effect. Aiternatively, high expenditure on some elements may even tend to retain those patients who wish to postpone release. However, a number of other equally beguiling explanations do exist. Suffice to say there is a substantial direct relationship which shows that the more generally spent on patients---the lower their discharge rate and this militates against the much stronger specific positive effect of higher expenditure on clinical and nursing staff producing the desired effect on discharge. It will also be seen that the actual size of the hospital has a negative effect on Cl, the overall cost index. That is to say, the cost per patient is higher in smaller hospitals. This might well be anticipated on general economy of scale principles but this analysis highlights the perhaps “hidden” indireet effects which the size of a hospital may have on its performance. The overall level of statig also has strong direct positive effects on two other performanct? variables, that is, Fe8 and Pe9, the annual number of out- and day-patient attendances (per Unit Catchment Population) respectively. We have already seen that the overall level of costing and indeed the hospital size will also, therefore, have indirect positive and negative effects respectively on these two perfurmanoe indices. Since, as we have already seen, the staffing level has a positive direct effect on the Discharge Rate, it is not surprising that it should show a similar direct effect on the out- and &y-patient indices since an increase in the level of out- and day-patient activity at a hospital is another aspect of the same policy which produces a high discharge rate. It represents an emphasis on day- and out-patient care rather than in-patient care. This is why we also see a strong negative association between the Discharge Rate and Pel, the number of In-patients per Unit Catchment Population. Clearly, a rapid discharge rate will cause a run-down in the number of permanent In-patients. (For comments on the playing of Pel, Pe8 and Pe9 against each other, see [ 18‘J.) We notice also that the index of inaccessibility of the hospital has a negative effect on the performance index Pe9, the number of day-patient at~n~n~s. This is quite clearly what one would expect on prima facie grounds but it is interesting to see such clear associations emerging and would seem to support the view advanced by Hoenig and Wamilton that the location of the ho~i~ in refation to the ~pulation it is serving can be an extremely important determinant of the way in ,which that hospital functions [193. This is, of course, one of the principal arguments for the establishment of psychiatric units in General Hospitals which are generally more accessible to the Catehment Papulation which they serve than is the
ease with the ~jority hospitals.
of the traditional
mental illness
CONCLUSION
At the end of the day we have a situation where a large number of candidate explainer variables and performance indices have been reduced to a very small number indeed; this residue having then been used in a representation of the causal process. This does not mean that all of the variables which do not appear in the picture are irrelevant as determinants or measures of the way in which a hospital functions. There is ample scope for further research into the effects of some of these variables. We have, in this p~~n~tion, attemp~d to err on the side of simplicity since one of the valid criticisms of path analysis studies at the present is a tendency to over-complexity. What we do claim, however, is that we have isolated, using national data, the crucial variables with respect to a determinant influence on the performance indices as we have chosen them. We have strong statistical evidence of the importance of the relationships between expenditure levels, staffing Ievel, discharge rates and the number of in-~ti~nt~ per unit catchment popuiation. We believe that this in it&f is justification for this approach. We have tried to explore and explain these relationships and this may give rise to further, more partieularistic, research in this area. What is paramount is the very strong relationship between the level of trained staff and the Pe measures of activity and achievement. In the balance the indieation would seem to be a con~n~ation on more staff per hospital rather than more beds. Nevertheless, it is quite clear that until precise data on re-admission is available one cannot really be certain of such conclusions. Further partieularistic research is. of course, essential for a full understanding of the processes at work. We appreciate that measures such as discharge and re-admission rate are in themselves rather inadequate measures of success. Indeed there will be disagreement about how far, for example, frequent discharge and re-admission, as opposed to continued in-patient treatment, constitutes “sueeess” for the individual patient, What perhaps is ultimately required is Further work on some measure of social competence before admission and after discharge. However, such data do not, at present, exist and. indeed, it is difficult to see how much data could be available on a National Scale. We have been concerned to examine the variations in the national data which is available to obtain an indication of the relationships which exist between such factors as stafing levels and the discharge rate. This will, we hope, assist in the selection of areas for detailed parti&ularistic research and also indicates the probable consequences of policy decisions relating to staffing ievels and other similar factors, without committing ourselves as to whether particular consequences are good or bad. REFERENCES
1.
Davies B. P. et al. Vuriatfons in Srrvic~s for the Aged. B&f. London. 1970. -^.--- G.
261
A model of psychiatric hospital performance 3. Mapes R. E. A. and Heywood R. T. The analysis of regional variation in infant and perinatal mortality rates. Oxford University Press for Nuffield Provincial Hospitals Trust, London, 1970. 4. Kalton G. The role of population surveys as a source of morbidity and other health data. Statistician 21, 301, 1972. 5. Susser M. Causal Thinking in the Health Sciences. Oxford University Press, Oxford, 1973. 6. Moser C. A. and Kalton G. Survey Methods in Social Investigations. Heinemann, London, 1971. 7. Mapes R. E. A. and Allan G. J. B. Path analysis: a cautionary note. Sot. Rev. 21, 137, 1973. 8. Parsons T. Suggestions for a sociological approach to the theory of organization. Admin. Sci. Q. 1, 63, 1956. 9. Blau P. M. and Scott W. R. Formal Organizations. Chandler, San Francisco. 1962. 10. Haas J. E. and Drabek T. E. Complex Organizations: A Sociological Perspective. Macmillan, New York, 1973.
APPENDIX:
SUMMARY
LISTS OF INDICES
11. Robinson D. Patients, Practitioners and Medical Care. Heinemann, London, 1973. 12. Etzioni A. Modern Organizations. Prentice-Hall, Englewood Cliffs, New Jersey, 1964. 13. Jones K. and Sidebotham R. Menta/ Hospitals at Work. Routledge & Kegan Paul, London, 1962. 14. Ullmann L. P. Institution and Outcome. Pergamon Press, Oxford, 1967. 15. D.H.S.S. The Facilities and Services of Psychiatric Hospitals 1970. Statistical Research Report Series No. 2, HMSO, 1972. 16. Van de Geer J. P. Introduction to Multioariate Analysis for the Social Sciences. W. H. Freeman, San Francisco, 1971. 17. Moores B. and Casmas S. (n.d.). Unpublished paper. 18. Mapes R. E. A. and Wilson B. J. My In press, 1975. 19. Hoenia J. and Hamilton Marian W. The De-seareaation of-the Mentally Ill. Routledge & Kegan Paul, L&tdon, 1969.
OF PERFORMANCE
AND
EXPLAINER
VARIABLES
Indices of L‘performance”
Pel Pe2 Pe3
No. of in-patients
per 1000 catchment population. No. of annual admissions per loo0 catchment population. Patients’ turnover rate =
admissions + discharges + deaths 2 x average availablestatfed
Pe4
beds
(all per annum).
Death rate annual deaths x loo. = 365 x daily average resident patients
Pe5 Pe5a
Discharge rates. Basic discharge rate annual discharges x 100. = 365 x daily average of resident patients
Pe5b
annual short-term patient discharges x loo. = 365 x daily average of short-term resident patients
PeSc
annual non-elderly patient discharges x loo. = 365 x daily average of non-elderly resident patients
Pe6 Pe7 Pe8 Pe9 PelO Pell
No. of new out-patients per annum No. of new day-patients per annum Total no. of out-patient attendances Total no. of day-patient attendances No. of out-patient attendances per No. of day-patient attendances per
per 1000 catchment population. per 1000 catchment population. per annum per 1000 catchment population. per annum per 1000 catchment population. 100 in-patient days. 100 in-patient days. Explainer
variables
Environmental
Ela-c
Indices of hospital accessibility from its catchment population.
Indices of local authority community services No. of welfare service social workers per loo0 catchment population. No. of mental health social workers per 1000 catchment population. No. of places per 1000 catchment population for the mentally ill provided by the local authority in workshops or day centres. No. of junior places per 1000 catchment population available in local authority training centres (including E2d special units). As E2d but for adult places. E2e
E2a E2b E2c
Indices of co-ordination between the hospital and the local authority Official co-ordination. E3a Actual contact. E3b Index of the extent of involvement of voluntary organizations. E4
R. E. A. MAPESand M. J. CLARKE
262 Professional
Prla Prlb Prla Pr2b Pr3a Pr3b Pr3c Pr3d Pr4
(number per 100 in-patients in each case) Consultant psychiatrists. Other psychiatric medical staff. Trained nurses. Other nurses. Psychologists. Psychiatric social workers. Therapists. Instructors/teaching staff. Domestic assistants and ward orderlies.
Ii7stitutional
Ila Ilb I2 I3 14 15a 15b I6
“6 of resident patients working in domestic and hospital service departments. 7< of resident patients working elsewhere, i.e. handicrafts. etc. 7; of patients in wards of 50 or more. Average y0 bed occupancy. Number of years elapsed since qualification of medical superintendent. y;, of beds with a space of less than 50 sq. ft. O’ of beds with a space of more than 60 sq. ft. Gisiting policy.
Institutional-costing
In-patients ICl IC2 IC3a IC3b IC3c ICM IC4
Cost of drugs. Total ward cost. Cost of pathology. Cost of pharmacy. Cost of ancilliary medicine. Medical service departments . Total in-patient cost.
Out-patients Cost ICOl ICO2 Total IC03 Cost ICO4 Total IC05
=
cost.
of drugs (equivalent to ICl). out-patient departments cost (equivalent to IC2). of treatment departments only (equivalent to IC3d). out-patient cost (equivalent to IC4).
Net total out-patient cost (IC04) total in-patient cost (IC4)
Socio. .medical SI
S2a S2b S3a S3b s3c s4 s5 S6
% of % of % of “//,of % of y/, of % of % of % of
catchment population who are male. male catchment population, over 15, who are married. female catchment population, over 15, who are married. male catchment population in executive and professional classes. male catchment population in other non-manual and skilled manual classes. male catchment population in unskilled and semi-skilled classes. admitted patients who are informal patients. admitted patients who are aged 65 or over. admitted patients who are male.
