Causation and Management of Disruptive Behavior in Long-Term Care Homes. Michael Stones Lakehead University

Transcription

1 Causation and Management of Disruptive Behavior in Long-Term Care Homes Michael Stones Lakehead University

2 This talk is about modeling disruptive behavior in long term care homes (LTCH). By disruptive behavior I mean aggregated scores of RAI 2.0 items on verbal abuse, physical abuse, socially inappropriate behavior and resisting care. Some researchers refer to this aggregate measure as aggression, which I believe is incorrect terminology. Dictionaries define aggression as threat or attack: however, it is by no means obvious that LTCH residents scoring positively on these items have such intent. Disruptive behavior is a preferred term because it is neutral about a resident's motive but refers instead to effects perceived by the staff of the home, which is what the RAI 2.0 measures.

3 Models of Disruptive Behavior There are two main models of disruptive behavior: A model linking that behavior with residents traits such as cognitive impairment, a phase within a dementing process, or personality; A model linking that behavior with situations that cause discomfort or distress.

4 Treatment Paradigms Treatment paradigms from the trait model rely heavily on pacification by antipsychotic medication. Nearly ¼ of new residents in Ontario's LTCH are on antipsychotics within a year despite warnings of serve side effects from the USA and Health Canada. Treatment paradigms with the situation model require situational evaluation and modification, with no known adverse side effects.

5 Problems in Statistical Modeling Statistical modelling of the antecedents and consequences of disruptive behavior in research on LTCH presents problems for two reasons: Inferences about causation in ordinary least squares and logistic regression flow only from independent to dependent variables; Assumptions about randomness apply only to residents. Both the preceding are incorrect and contribute to error in statistical modelling.

6 Inferences about Causation Inferences about paths from X to Y can include: X as a cause of Y... Y as a cause of X... X and Y with bidirectional causation... X Y Y X X Y Realistic inferences about the causal relationships between disruptive behavior and antipsychotics are bidirectional. Previous research showed that disruptive residents are 5* more likely to receive antipsychotics than non-disruptive residents. However, the effect of medication is to lower disruptive behavior.

7 Inferences about Causation Statistical programs that model bidirectional effects include path analysis and structural equation modelling.

8 Randomness All RAI research I'm acquainted with assumes that individual residents are a random sample drawn from the same population. This assumption is patently false. Residents selected or self-selected for one institution differ from those selected or self-selected for another institution. Also, the treatment of residents differs across institutions. Statistical programs that allow for randomness at multiple levels include MIXED linear analysis in SPSS and SAS.

9 Purposes of the research One purpose was to illustrate the use of LISREL path analysis to model antecendents and consequences of disruptive behavior that included a bidirectional relationship between disruptive behavior and antipsychotic medication use. A second purpose was to compare LISREL models with and without control of LTCH treated as a random variable. The model without such control analyzed RAI 2.0 scores. The model with such control analyzed the RAI 2.0 residuals after control for LTCH as a random variable in MIXED linear analyses.

10 METHODS Participants 1488 residents of LTCH in Ontario as part of the RAIHIP study in 2000/1. Measures The measures included variables found in previous research to predict the Disruptive Behavior Scale: (1) Cognitive Performance Scale, (2) ADL Hierarchy, (3) Pain Scale), (4) Index of Social Engagement, (5) Depression Rating scale, (6) Delirium, (7) Incontinence, (8) Antipsychotic Medication.

11 MIXED Linear Analysis In separate analyses with each measure as dependent variable, every MIXED linear analysis showed significant effects with LTCH as random independent variable. A conclusion is that LTCH have effects on a wide array of outcome measures from the RAI 2.0. The scores used in analysis that controlled for LTCH were unstandardized residuals. The distributions of these residuals generally were closer to normal than the RAI 2.0 scores.

12 LISREL Path Analysis Models with and without control for LTCH showed good fit to the data. However, use of modification index showed differences between the models with best fit. Index Ҳ²[14] Model with Control of LTCH (p <.009) Model without Control of LTCH Ҳ²[13] (p >.05) GFI AGFI RMSW

13 Path Model with LTCH Controlled: Direction of Significant Parameters Predictor Predicted Measure Depress Delir AntiP Disrup SocE Cognitive Imp ADL - - Incontinence Pain + - Depression Delirium + Antipsychotic - - Disruptive + - Social Engage

14 Path Model without LTCH Controlled: Direction of Significant Parameters Predictor Predicted Measure Depress Delir AntiP Disrup SocE Cognitive Imp ADL - - Incontinence Pain + - Depression + + Delirium + Antipsychotic - - Disruptive + - Social Engage +

15 Pain: The negative parameter estimate for pain on disruptive behavior reflects a quadratic trend.

16 Discussion: Antecedents & Consequences of Disruptive Behavior With RAIHIP 2.0 LTCH data, path analysis showed the antecedents of disruptive behavior to include Sources of distress associated with treatable conditions of delirium, depression, and pain; Cognitive impairment. Likelihood of antipsychotic medication use increases with the level of disruptive behavior and the use of antipsychotic medication decreases level of disruptive behavior. The consequences of both disruptive behavior and antipsychotic medication include lower levels of social engagement.

17 Discussion: LTCH as a random variable Inclusion of LTCH as a random variable was supported by significant effects on every variable included in the analyses. Although findings from path analysis were similar with or without scores that control for LTCH as a random variable, they were not the same. An important difference concerns the relationship between depression and social engagement. With control for LTCH, the path was Depression Social Engagement but without such control the path was Depression Social Engagement. Implications are that without such control, inferences about causal relationships from RAI data may be in error.