TAWS PAPER 02 OCCUPANCY, ADMISSIONS, CONFLICT AND CONTAINMENT

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1 TAWS PAPER 02 OCCUPANCY, ADMISSIONS, CONFLICT AND CONTAINMENT AUTHORS: Len Bowers DATE FINALISED: 18 December 2007 VERSION NUMBER: 1 CHANGES FROM PREVIOUS VERSIONS: not applicable INTERNET PAPERS This series was developed to report and make available findings from the analysis of the dataset that in themselves did not merit submission in a peer reviewed scientific journal, but may prove useful to other researchers. Full listings of published papers, and a copy of the final report to project funders, can be found on under research/tompkins Acute Ward Study. BACKGROUND A longitudinal study was conducted on 14 acute inpatient psychiatric wards and three Psychiatric Intensive Care Units in a single NHS Trust. The study Trust served a population of 650,000 in three inner London boroughs, each of which had high proportions of ethnic minority residents (approximately 60%, compared to the England and Wales average of 12%), and high levels of social deprivation (all fell within the category of the 10% most deprived areas in the country). Data were drawn from officially collected information on admissions, adverse incidents, workforce deployment and training; researcher collected information included end of shift reports from the wards, repeated interviews of patients, ward managers and consultant psychiatrists, and repeated waves of questionnaires from patients and ward staff. The study was undertaken in two phases, the first retrospective and utilising officially collected data only ( ), the second prospective and including both researcher and officially collected data ( ). The study brought together four years of officially collected data on adverse incidents on the wards and patient admissions/discharges; two years of prospectively collected research data on conflict and containment on the wards, composed of approximately 15,000 end of shift reports, 119 patient interviews, 77 ward manager interviews, and 43 consultant psychiatrist interviews. In addition a number of questionnaires were collected on a repeated basis throughout the study. Overall response rates for the prospectively collected data were fair, with 45% of the potential total end of shift reports collected. Precise estimates of response rates for other items are not possible to provide, as numbers of staff in post fluctuated over time during the study. For interviews of patients, a 93% response rate was attained, as replacements were sampled for those patients who did not wish to participate. The same process could 1

2 not be used for staff as, for example, each ward had only one ward manager. Nevertheless the response from ward managers was excellent, with only a few missed interviews (96% reponse rate), whereas interviews with consultant psychiatrists were much more difficult to obtain (46% response rate). These data are complex, cover a large period of time, and can be analysed in many different ways, including: computer-aided content analyses of interviews; cross sectional time-series analysis; pooled cross sectional analysis; mixed method, multiple case comparison; a contrast analysis between patients who receive different types of care logistic regression; and many more. A full account of the methodology can be found in the final report to the funders, on That report includes definitions and descriptions of all variables, with means, standard deviations, etc. for the sample. AIM To assess the relationship of conflict and containment (top and mid range variables) to patient admission and occupancy characteristics, utilising shifts and weeks as the time segments. INSTRUMENTS The Patient-staff Conflict checklist, and officially collected patient data from the Trust's Patient Administration System. See the project final report for more details of both. ANALYSIS PCC data was available for 14,114 valid shifts (both date and shift present). Patient data was imported into MS Access and queried to produce tables of admissions and occupancy by shift. This was then exported and merged with the PCC data in STATA. The patient data available included age, gender, diagnosis and ethnicity. A second file was created by collapsing the former file by ward and week, controlling for shift type, and discarding any weeks for which there were fewer than seven PCCs. This resulted in a file consisting of 954 ward weeks of data. Conflict and containment counts in this dataset were collapsed into means and rounded to provide counts suitable for analysis. Spearman correlations were used for univariate analysis, as dependent variables were skewed. Stepwise Poisson regression was used to model each conflict and containment variable, admissions and occupancy being modelled separately because of collinearity between the two. Other details of the analysis are provided together with the findings below. 2

3 FINDINGS Conflict and containment Rates of different forms of conflict and containment over the study period are presented in Table 1, based on 15,006 PCC-SRs collected. These rates are very close to the national norms from the City-128 study of 136 acute psychiatric wards, indicating that the study wards are representative in relation to their rates of conflict and containment events. 3

4 Table 1. Mean rate of conflict and containment events per shift (excluding self-harm). Not standardised to occupancy. Mean SD Verbal aggression Physical aggression against objects Physical aggression against others Total aggression Smoking in a no smoking area Refusing to eat Refusing to drink Refusing to attend to personal hygiene Refusing to get up and out of bed Refusing to go to bed Refusing to see workers Total rule breaking Alcohol use (suspected or confirmed) Other substance misuse (suspected or confirmed) Total substance use Attempting to abscond Absconding (missing without permission) Absconding (official report) Total absconding Refused regular medication Refused PRN medication Demanding PRN medication Total medication related Given PRN medication (psychotropic) Given IM medication (enforced) Sent to PICU or ICA Seclusion Special observation (intermittent) Special observation (constant) Show of force Physically restrained Time out Total conflict Total containment When standardised to 20 patient occupancy, these figures are slightly higher, but remain broadly comparable to City 128 norms. All these variables, including the totals, are highly skewed, as most shifts have few or no incidents of conflict, and shifts with more conflict/containment incidents are 4

5 progressively rarer. As examples, the histograms for total conflict and total containment per shift are displayed in Figures 1 and 2 below. Figure 1. Histogram of total conflict during the shift. Density TOTFLICT Figure 2. Histogram of total containment during the shift. 5

6 Density TOTTAIN Patient characteristics Table 2 shows the admission and occupancy data that was matched to the validly dated PCC data (n = 14,114 shifts). The largest single group were patients with schizophrenia. As their proportion of the occupancy was larger than that of admissions, this indicates they had longer than average stays. The next largest group was people with affective disorders, who tended to have shorter than average lengths of stay. Nearly one in ten admissions was for a disorder caused by substance use, but length of stay for this group was shorter. No other group comprised more than 5% of admissions or occupancy. The mean age of admissions was years (sd 3.85), and 60.56% were male. Table 3 displays patients' ethnicity, and shows that less than one third of patients were white British, the other main groups being African, other white, other black, Bangladeshi and Caribbean. Differences in proportions between admissions and occupancy are not as large as in the diagnosis table, but a tendency for some (but not all) ethnic minority groups to have had slightly longer stays, and white patients to have had shorter stays is visible. These differences may, of course, be accounted for by demographic and diagnostic differences between ethnic groups. Table 2 Diagnosis 6

7 Occupancy and admissions from data matched to valid shift PCCs Diagnosis (ICD-10) Admissions Occupancy (days) n % n % F00s (Organic) % % F10s (Disorders due to substance use) % % F20s (Schizophrenia) % % F30s (Affective disorder) % % F40s (Neurotic disorders) % % F50s (Physiologically caused) % % F60s (Personality disorder) % % F70s (Mental retardation) % % F80s (Developmental disorder) % % F90s (Child/Adolescent onset disorder) % % Other % % No diagnosis recorded % % Total % % Table 3 Ethnicity Occupancy and admissions from data matched to valid shift PCCs Ethnicity Admissions Occupancy (days) n % n % British % % African % % Any other White background % % Any other Black background % % Bangladeshi % % Caribbean % % Indian % % Not stated % % Any other Asian background % % Any other ethnic group % % Irish % % Pakistani % % White and Black Caribbean % % White and Black African % % Any other mixed background % % White and Asian % % Chinese % % Total % % Relationship of conflict and containment to patient characteristics 7

8 Both shift type, occupancy and admissions were related to conflict and containment, as shown in Tables 4 and 5. The only variable not associated with shift type was selfharm, although this is a tentative result as this item was measured in two different ways at different times during the study. However self-harm in the City-128 study also behaved differently from other conflict variables. Most of these events are lower during the night shift, except for medication related conflict, and total containment. All variables except self-harm are significantly associated with occupancy, i.e. the number of patients present on the ward on each shift who are therefore available to perform these behaviours or be subject to containment. However the direction of these associations is not always positive, with total containment and total aggression being inversely associated with occupancy. Admissions during the shift were also significantly related to conflict and containment, except for rule breaking. Untangling these relationships is complex because wards are of different sizes, or to state this another way occupancy is confounded by ward, and ward associations with conflict and containment could appear in the analysis as occupancy relationships with conflict and containment. Table 4 Mean rates of conflict and containment by shift type, with Kruskal-Wallis equality of proportions test Shift Total Total rule aggression breaking Total substance use Total absconding Total medication related Total selfharm (z score) Total conflict Total containment Morning Afternoon Night Chi square df p <0.001 <0.001 <0.001 <0.001 < <0.001 <0.001 Table 5 Spearman correlations between conflict and containment and occupancy and admissions Conflict & containment Occupancy Admissions rho p rho p Total aggression < <0.001 Total rule breaking < Total substance use < Total absconding < <0.001 Total medication related < Total self-harm (z score) Total conflict < Total containment < <0.001 Overleaf are Tables 6 and 7, showing the univariate relationships between patient characteristics, and the conflict and containment variables. Admission characteristics 8

9 are associated with other variables, most strangle with total containment and with total absconding. Occupancy variables are much more strongly related to conflict and containment, however this analysis is problematic because most of the occupancy variables are themselves associated with total occupancy during the shift, and total occupancy is the number of people who are available to be aggressive, abscond etc. 9

10 Table 6 Univariate associations by Spearman correlations between conflict and containment variables, and admissions and their characteristics. Significant associations are highlighted in red. Total medication Total self-harm (z Total aggression Total rule breaking Total substance use Total absconding related score) Total conflict Total containment r p r p r p r p r p r p r p r p Number of admissions during shift Number of male admissions African Any other Asian background Any other Black background Any other White background Any other ethnic group Any other mixed background Bangladeshi British Caribbean Chinese Indian Irish Not stated Pakistani White and Asian White and Black African White and Black Caribbean F00s (Organic) F10s (Disorders due to substance use) F20s (Schizophrenia) F30s (Affective disorder) F40s (Neurotic disorders) F50s (Physiologically caused) F60s (Personality disorder) F80s (Developmental disorder) F90s (Child/Adolescent onset disorder) Other No diagnosis recorded

11 Table 6 Univariate associations by Spearman correlations between conflict and containment variables, and occupancy characteristics. Significant associations are highlighted in red. Total aggression Total rule breaking Total substance use Total absconding Total medication related Total self-harm (z score) Total conflict Total containment r p r p r p r p r p r p r p r p Occupancy mean age Number of males African Any other Asian background Any other Black background Any other White background Any other ethnic group Any other mixed background Bangladeshi British Caribbean Chinese Indian Irish Not stated Pakistani White and Asian White and Black African White and Black Caribbean F00s (Organic) F10s (Disorders due to substance use) F20s (Schizophrenia) F30s (Affective disorder) F40s (Neurotic disorders) F50s (Physiologically caused) F60s (Personality disorder) F70s (Mental retardation) F80s (Developmental disorder) F90s (Child/Adolescent onset disorder) Other No diagnosis recorded

12 Two modelling exercises were then conducted with conflict and containment as dependent variables, using poisson regression: one for admissions and the second for occupancy. In these models, allowance was made for the clustering of results by ward, and shift type was included to control for the effects of differences between morning, afternoon and night shifts. The number of potential variables was decreased by collapsing all ethnicities and diagnoses comprising less and 3% of admissions into 'other' categories, and these were then dropped from the analyses as a reference category. A stepwise procedure was used obtain the final models, with p = 0.05 as the inclusion criteria. In the models of occupancy data, in order to separate the effect of individual variables from that of occupancy itself, both ethnicity and diagnoses were expressed as proportions for each shift, and total occupancy was used as the exposure variable. The models resulting from this exercise are displayed in Tables 7 & 8 below. Table 7. Models of conflict and containment in relation to admission characteristics IRR 95% CI p Total aggression F40s (Neurotic disorders) Night shift <0.001 Total rule breaking Afternoon shift Night shift F10s (Disorders due to substance use) F40s (Neurotic disorders) Total substance use Afternoon shift <0.001 African ethnicity F10s (Disorders due to substance use) F30s (Affective disorder) Total absconding Night shift <0.001 F10s (Disorders due to substance use) F30s (Affective disorder) Total medication related Night shift <0.001 Indian ethnicity Total conflict F40s (Neurotic disorders) Total containment Indian ethnicity F10s (Disorders due to substance use) F30s (Affective disorder) F60s (Personality disorder) Table 8. Models of conflict and containment in relation to occupancy characteristics. 12

13 IRR 95% CI p Total aggression Night shift <0.001 Any other black background <0.001 Mean age Total rule breaking Afternoon shift Night shift Indian ethnicity F10s (Disorders due to substance use) Total substance use Afternoon shift <0.001 Indian ethnicity Total absconding Night shift <0.001 Bangladeshi ethnicity <0.001 Total medication related Night shift <0.001 Total conflict Indian ethnicity Mean age Total containment Any other white background Mean age F10s (Disorders due to substance use) F30s (Affective disorder) F60s (Personality disorder) These relationships are not straightforward to interpret, especially with respect to ethnicity, but also with regard to diagnosis. For both diagnoses and ethnicities are not randomly distributed across the different wards, as the wards represent local communities and the psychiatric teams that serve them. Some feature of teams or wards might thus be confounded with the proportions of an ethnicity or a diagnosis. They reflect the univariate analyses, in that plainly containment rates are more closely allied to admission and occupancy characteristics than conflict. However they give no grounds for thinking that ethnic minority patients are more likely to be subject to containment methods. Gender is noticeably absent from any of the models. The same analysis was completed after aggregating the data by week, retaining only those ward weeks where more than 7 PCCs had been returned (n = 954). The results are displayed in Tables 9 and 10. Table 9. Models of conflict and containment in relation to admission characteristics (ward week as time segment) 13

14 IRR 95% CI p Total aggression Nil significant Total rule breaking Nil significant Total substance use Nil significant Total absconding F10s (Disorders due to substance use) <0.001 F20s (Schizophrenia) Total medication related Any other black background <0.001 F10s (Disorders due to substance use) <0.001 F60s (Personality disorder) <0.001 Total conflict Indian ethnicity F30s (Affective disorder) Total containment Bangladeshi British Indian <0.001 F10s (Disorders due to substance use) <0.001 F30s (Affective disorder) F40s (Neurotic disorders) <0.001 F60s (Personality disorder) Table 10. Models of conflict and containment in relation to occupancy characteristics (ward week as time segment) 14

15 IRR 95% CI p Total aggression Any other black background <0.001 Indian Mean age Proportion male F30s (Affective disorder) Total rule breaking Indian ethnicity F10s (Disorders due to substance use) Total substance use Indian ethnicity <0.001 Total absconding Bangladeshi ethnicity <0.001 Total medication related Nil significant Total conflict Indian ethnicity Total containment Any other white background Mean age F10s (Disorders due to substance use) F30s (Affective disorder) To facilitate comparisons across the different sets of poisson modelling results, these are summarised in Table

16 Table 11. Poisson modelling results summary on the relationship between conflict/containment and patient characteristics ADMISSION/SHIFT IRR ADMISSION WEEK IRR OCCUPANCY/SHIFT IRR OCCUPANCY/WEEK IRR Total aggression Total aggression Total aggression Total aggression F40s (Neurotic disorders) Nil significant Night shift Any other black background Night shift Any other black background Indian Mean age Mean age Proportion male F30s (Affective disorder) Total rule breaking Total rule breaking Total rule breaking Total rule breaking Afternoon shift Nil significant Afternoon shift Indian ethnicity Night shift Night shift F10s (Disorders due to substan F10s (Disorders due to substance Indian ethnicity F40s (Neurotic disorders) F10s (Disorders due to substan Total substance use Total substance use Total substance use Total substance use Afternoon shift Nil significant Afternoon shift Indian ethnicity African ethnicity Indian ethnicity F10s (Disorders due to substance F30s (Affective disorder) Total absconding Total absconding Total absconding Total absconding Night shift F10s (Disorders due to substance Night shift Bangladeshi ethnicity F10s (Disorders due to substance F20s (Schizophrenia) Bangladeshi ethnicity F30s (Affective disorder) Total medication related Total medication related Total medication related Total medication related Night shift Any other black background Night shift Nil significant Indian ethnicity F10s (Disorders due to substance F60s (Personality disorder) Total conflict Total conflict Total conflict Total conflict F40s (Neurotic disorders) Indian ethnicity Indian ethnicity Indian ethnicity F30s (Affective disorder) Mean age Total containment Total containment Total containment Total containment Indian ethnicity Bangladeshi Any other white background Any other white background F10s (Disorders due to substance British Mean age Mean age F30s (Affective disorder) Indian F10s (Disorders due to substan F10s (Disorders due to substan F60s (Personality disorder) F10s (Disorders due to substance F30s (Affective disorder) F30s (Affective disorder) F30s (Affective disorder) F60s (Personality disorder) F40s (Neurotic disorders) F60s (Personality disorder)

17 Relationship of conflict and containment to over and under occupancy For 83% of shifts, wards were at full occupancy or below, the rest of the time they were over occupied. In the study Trust, over occupation was dealt with in four ways. The first, which would not show in our data, was to make a full transfer of patient(s) to another ward which was not currently full. Such transfers add complexity of the management of care, forcing the responsible community clinicians to work with different wards, attend different meetings on different days to do so, and running the risk of poor communication of the patients current care plan from one ward to another. The second would be to send patients on leave to their home address, sometimes requesting them to attend the ward during the day, utilising the ward as a sort of day care facility. This method would also not show up in our data, as patients on leave were not counted in our occupancy statistics. The third, was to send patients to sleep over on another less full ward overnight, and return for care during the day. This resulted in more patients being on the ward during the day than there were beds. Although it resolved some workload and communication issues, it could still mean disjunctions in care overnight, and meant that some patients, potentially tired or drowsy from medication side effects, had no bed to rest on during the day, and no easy access to their personal possessions. The fourth solution was to allow patients to sleep on sofa's or camp beds on the ward. This meant patients at least had continuity of care, albeit with nowhere to rest during the day. Both the third fourth and sometimes second solutions led to overcrowding on the wards during the day, with more patients present than the ward had been designed for. Crisis teams were set up during the course of the study (at two hospitals at about week 64, and at the the third at about week 92), and indeed, occupancy fell possibly as a result of this. However a new policy allowing transfers between the three hospitals was implemented at about week 100, and this also may have contributed to the fall. Smoothed data shows a statistically significant decline (see Figure 3). Mean occupancy across all the wards within weeks was generally low, i.e. the wards were overall under occupied, and any crises in occupancy tended to be brief and confined to small parts of the overall service provision. Figure 3. Smoothed over and under occupancy figures by week 17

18 lowess overunderocc week week The nature of the relationship of over/under occupancy and conflict and containment is not easy to determine. Inspection of the scattergrams appears to imply a curvilinear relationship, with a peak of conflict at maximum occupancy, and lower conflict with both extreme over and under occupancy (see Figure 4). However this is an illusion based on the fact that more shifts were at near to full occupancy, therefore there was more chance of a shift with an extreme number of conflict events being reported. Figure 4. Scattergram of total conflict by over and under occupancy 18

19 TOTFLICT overunderocc Curve fitting tests were carried out using the curvefit procedure of SPSS. Although many nonlinear formulae provided a statistically significant fit to the data (as did in every case a linear model), r-squared values were uniformly small (<= 0.005), therefore none were to be preferred to a standard linear model. The data therefore provided no evidence that overcrowding led to increases in conflict or containment over and above those caused by simply having more patients on the ward who could behave in a difficult fashion. DISCUSSION Occupancy variables appear to be more predictive than admission variables, in that there are generally more and stronger associations. In addition, although patient characteristics seen through the lens of admissions are very similar to those seen through occupancy, they are not the same. And as can be seen for the findings reported here, results using admissions and occupancy do differ, although there are some repetitive similarities. The City 128 analysis was based on admissions only, as no occupancy data was available, and results from that analysis will be biased towards admission rather than occupancy associations. The main finding here is that containment seems to be more associated with patient characteristics than conflict is. Maybe this is about staffs risk assessments and use of preventive containment. However this was not the same in City 128 data, where univariate analysis showed conflict more related to patient characteristics and multivariate no real difference. These two sets of results do not match, even on this issue. 19

20 Overcrowding is not specifically related to conflict and containment, except in the sense of providing more patients who can engage in difficult behaviour. Finally, the question arises as to which dataset contains the greater amount of error, affecting the results of the above reported analysis: the official data inputted largely by ward clerks with diagnoses recorded by junior doctors, or the PCC data collected by ward nursing staff. CONCLUSIONS Occupancy and admissions in terms of both numbers and characteristics, are related to conflict and containment use. However those relationships are neither strong nor entirely consistent, suggesting that other factors may be more important. The nature of the relationship appears to be linear, or at least curve fitting does not produce a significantly superior fit, suggesting that overcrowding does not lead to exponential rises in conflict or containment. CREDIT The data collection on which this paper is based was funded by NIHR SDO. REFERENCES 20