Category Archives: Uncategorized

 

 

Readmissions for mental health reasons are common.  The consequences of rapid readmission (30-day) to inpatient psychiatric units are significant from not only a clinical and public health perspective, but also specific to the disruption to the patient’s and their families’ lives (Vigod et al., 2015).  Vigod et al. (2015) note, to apply appropriate interventions, clinicians must be able to identify those who might be at greater risk for readmission and who may benefit from interventions to reduce readmission risk.

 

Due to limited research regarding methods to quantify risk for readmission, the authors focused on deriving and validating a clinical risk index to predict individual risk for rapid readmission.  The goal was to use this risk index to help identify high-risk individuals and offer interventions and support during the hospitalization and after discharge.  The authors focused on four categories of predictive factors:  socio-demographic, prior health care utilization, basic clinical and administrative information from hospital admissions, and data from psychiatric rating scales administered during an inpatient psychiatric admission.  Overall, the authors considered the feasibility of collecting the data to be used in the index as well as the predictive capabilities.

 

Vigod et al. (2015) collected data from individuals discharged from an acute psychiatric facility in Ontario, Canada between 2008 and 2011.  Population-level socio-demographic and health administrative data were collected from different databases.  The cohort was aged 18 or older and consisted of individuals hospitalized for greater than 72 hours.  The outcome variable was psychiatric readmission to any hospital in Ontario within 30 days where a mental health condition was the most responsible diagnosis.  Predictor variables were socio-demographic (age, education, martial status), prior health service use (lifetime mental health admissions, outpatient mental health visits, medical comorbidity), index hospitalization variables (threat to self, threat to others, unable to care for self, primary diagnosis, personality disorder, planned and regular discharge, length of stay), and clinical assessment characteristics (stressful life events, positive symptoms, negative symptoms, anxiety symptoms, aggressive behavior, self care levels, interpersonal conflict, and control interventions).

 

The authors used a split-sample method to build and validate the predictive models.   Thus, multivariable logistic regressions were used to determine the best-fit model, as well as assess for internal validity.  Four models were assessed in order to identify the best model to predict 30-day readmission.  The rational for assessing four models is centered on the goal of creating a model that not only best predicted readmission, but also was feasible to obtain data related to the predictor variables.  Model 1 was the most basic, only assessing socio-demographic variables.  Model 2 was more comprehensive assessing socio-demographic variables and prior health service variables.  Model 3 added basic clinical and administrative data related to the index admission. And Model 4, the most complex, added specific scores from clinical scales and assessment protocols during a psychiatric admission.  Model 4 was considered difficult to standardize as no uniform clinical information system was utilized within the sample.   Once the model was established, it was converted into a risk index system.

 

Overall, there were approximately 65,000 index admissions and the 30-day readmission rate was 91.9% (N = 6044) for the sample.  The highest risk period for readmission was within the first year after discharge, with minimal variation between the years assessed.  The average age for the sample was 44, with about half being female and most participants living in an urban area (89%).  Almost 2% were homeless at discharge and about one-quarter was employed. The most common diagnoses were Major Depressive Disorder and psychotic disorders, followed by Bipolar Disorder.

 

Results indicated that predictive capacity was poor with only socio-demographic variables (Model 1), and improved slightly when prior health service variables were included (Model 2).  With Model 3, adding clinical and administrative variables improved the discriminative capacity.  Adding more specific clinical variables resulted in significant improvement, yet discriminative capacity only increased slightly (Model 4).  Thus, the authors selected Model 3 to create the risk index, since the variables are easily collected and computed in most clinical settings.

 

The authors converted Model 3 into a risk index, and created an acronym (READMIT) to organize and identify the predictive variables.  More specifically, the variables within the model are:  R, history of admissions; E, emergent nature of index admission, specifically, harm to self, harm to others, inability to care for self; A, age; D, diagnoses of psychosis, bipolar, and personality disorder, and unplanned discharge; M, medical comorbidity; I, intensity of outpatient and emergency department use prior to admission; and T, time in hospital (Vigod et al., 2015, p. 208).  The range of scores for READMIT is 0 to 41.   Notably, a READMIT score increase of 1 point increased the odds of 30-day readmission by 11%.  Expected probability for the READMIT index ranged from 2% with a score of 0 to 49% for a score of 41.  See the Table 1 below (reproduced from Vigod et al., 2015, p. 211) for a summary of points allocated per risk factor.

 

The READMIT score quantifies 30-day readmission risk after hospitalization.  Consistent with existing literature, one or more previous admission is the most consistent predictive variable for readmission (Durbin, Lin, Layne, & Teed, 2007; Vigod et al., 2015).  Other risk factors identified by Durbin et al. (2007) related to rapid readmissions are younger age, forensic background, low family support, severe mental illness, and leaving the hospital against medical advice.  Here, READMIT also considers medical comorbidity, thus highlighting patient complexity.  Though the READMIT index only reported moderate predictive capacity, such is indicative of the unpredictability of events that occur after discharge that could mitigate or exacerbate risk.  Thus, it is not perfectly discriminative.

 

Overall, this study utilized population based socio-demographic and health service variables to identify risk factors related to rapid readmission.  Further, the authors indicated that the data are easily collected, which makes this index a feasible tool to identify those individuals at higher risk.  The goal of READMIT was to assess risk for readmission while individuals are in the hospital.  As a result, interventions are intended to be given prior to discharge in order to reduce the risk of readmission after discharge.  The authors note that READMIT could be used to indicate individuals in need of further assessment or specific intervention directed at modifiable risk variables either in the hospital or post-discharge.  It could also be used in research to identify post-discharge interventions designed to reduce readmission risk or to align resources for prevention efforts to areas or targeted populations of greater risk.  Overall, the authors created a risk index that moderately predicts readmission rates; such efforts can be useful specific to inpatient and post-discharge interventions.  The goal is to reduce readmission risk, yet it is difficult to impact risk if we are not able to identify those at greater risk.  In short, the READMIT score is a step in the direction of identification, with a focus on feasibility and predictive function.

 

References
Durbin, J., Lin, E., Layne, C., & Teed, M. (2007). Is readmission a valid indicator of the quality of inpatient psychiatric care?  Journal of Behavioral Health Services & Research, 34(2), 137-150.

Vigod, S. N., Kurdyak, P. A., Seitz, D., Herrman, N., Fung, K., Lin, E.,…Gruneir, A. (2015). READMIT: A clinical risk index to predict 30-day readmission after discharge from acute psychiatric units. Journal of Psychiatric Research, 61, 205-213.

 

 

Table 1

READMIT index variables and assigned points.
Risk Factor/Variable	Value	Points
(R) Repeat Admission Prior to index	0	0
	1 -2	2
	3-5	5
	6 or more	7
(E) Emergent Admission	Threat to others	0 No, 1 Yes
	Threat to self	0 No, 1 Yes
	Unable to care for self	0, No, 2 Yes
(A) Age	Older than 94	0
	85-94	1
	75-84	2
	65-74	3
	55-64	4
	45-54	5
	35-44	6
	25-34	7
	18-24	8
(D) Diagnosis and Discharge	Alcohol or substance	0
	Depression	2
	Psychosis or Bipolar	4
	Other	3
	Personality Disorder	0 No, 2 Yes
	Unplanned Discharge	0 No, 5 Yes
(M) Medical Comorbidity*	0	0
	1-2	1
	3 or more	2
(I) Intensity	Outpatient visits	0 for less than 2 2 for two or more
	Emergency room visits	0 none, 3 for one or more
(T) Time in Hospital	More than 28 days	0
	15-28 days	3
	Less than 14	4

* See Vigod et al. (2015) for coding of specific medical conditions and diseases.

 

 

Dannie S. Harris, M.A., M.A., M.A.Ed., Ed.S.
WKPIC Practicum Trainee

 

 

Dropout and early termination in therapy is a concern for many practicing psychologists or therapists.  Research indicates that 20 percent of clients will terminate therapy prematurely (Chamberlin, 2015).  Furthermore, Swift and Greenberg (2012) found that one in five clients will dropout before completing therapy.  So the question becomes, what are the common reasons for early termination and what can the practitioner do to influence this trend?  Briefly, according to Dr. Greenberg (as cited in Chamberlin, 2015) some of these common factors could be easily addressed.  For example, clients may have unrealistic assumptions about therapy or they may not fully understand the roles of client or therapist.  They also may not understand the timeline or commitment needed.  Additionally, some clients may have more practical problems, such as childcare or transportation difficulties.  Finally, clients may experience anxiety about discussing feelings and/or traumatic, emotional experiences.

 

In their book, Premature Termination in Psychotherapy, Swift and Greenberg offer eight empirically supported strategies (listed below) to help clients stay on track.

1. Provide role induction.  Here the clients are offered education on the process of therapy, as well as, clarify client and therapist expectations.
2. Incorporate client preferences into the treatment decision-making process.  This will help balance treatment options and will foster a client’s investment in therapy.
3. Help plan for appropriate termination.  Provide an estimated timeline for treatment; also allow open discussion about termination and endpoints that indicate the end of therapy.
4. Provide education about patterns of change.  Preparing clients for emotional setbacks is necessary, as well as discussing the initial improvements and thinking therapy is done.
5. Strengthen early hope.  Hope fosters commitment, and as a result, clients are more likely to continue and work past setbacks.
6. Enhance motivation for treatment.  Address motivation from session to session; utilizing techniques of motivational interviewing may also help clients remain in therapy.
7. Foster the therapeutic alliance.  Foster and develop basic therapeutic skills, as well as monitor and repair ruptures in the alliance.
8. Discuss treatment progress with your clients.  Providing feedback through discussion or objective self-report may help gauge progress and identify problems before clients dropout.

 

Overall, the strategies listed above provide simple interventions that have shown to mitigate dropout rates.  Often these strategies are not emphasized in training, but have shown to be effective in helping clients remain in treatment.

 

References:
Chamberlin, J. (2015).  Are your clients leaving too soon? Monitor on Psychology, 46(4), 60-63.

 

Swift, J. K., & Greenberg, R. P. (2012). Premature discontinuation in adult psychotherapy: A meta-analysis. Journal of Consulting and Clinical Psychology, 80, 547-559.

 

Swift, J. K., & Greenberg, R. P. (2014). Premature termination in psychotherapy: Strategies for engaging clients and improving outcomes. Washington DC: American Psychological Association.

 

Dannie S. Harris, M.A., M.A., M.A.Ed., Ed.S.
WKPIC Practicum Trainee

 

 

Diagnosing Autistic Spectrum Disorder: Differences Between Boys and Girls?

 

A recent study conducted by researchers at the Kennedy Krieger Institute in Baltimore, MD, has found that girls are diagnosed with Autistic Spectrum Disorder (ASD) later than boys. Data was obtained by reviewing the institute’s Interactive Autism Network, which is an online registry that includes nearly 50,000 individuals and family members affected by ASD.  The researchers examined gender differences regarding the age of an ASD diagnosis and symptom severity. Of the participants in the registry, the age of diagnosis was available for 9, 932 children. Of the participants in the registry, 5,103 were available to be assessed for symptom severity as they had completed the Social Responsiveness Scale, an instrument that assesses the presence and severity of social impairments.

 

The data review yielded results stating that girls were diagnosed with Pervasive Developmental Disorder, a type of ASD, at a mean age of 4.0 years; boys were diagnosed with it at 3.8 years. Girls were diagnosed with Asperger’s Syndrome, which affects language and behavioral development, at a mean age of 7.6 years, as compared to 7.1 years for boys.

One possible explanation is that females often exhibit less severe symptoms than males; therefore ASD is often less recognizable with girls than boys. The researchers suggest that girls tend to struggle more with issues related to social cognition and impairments in interpreting social cues, while boys tend to exhibit more severe mannerisms, such as repetitive behaviors (e.g., hand flapping) and/or highly restricted interests.  The researchers suggest improving screening methods as a way to diagnosis ASD more effectively, in addition to increasing public awareness.

 

Faisal Roberts, M.A.

WKPIC Doctoral Intern

 
Nauert PhD, R. (2015). Autism Diagnosis Made Later in Girls. Psych Central. Retrieved on April 30, 2015, from http://psychcentral.com/news/2015/04/29/autism-diagnosis-made-later-in-girls/84057.html

Review by:

Faisal Roberts M.A.

WKIPC Psychology Intern

 

The presence of a personality disorder (PD) can profoundly impact an individual’s quality of life in addition to the management of comorbid mental health issues, therefore screening for PDs should be an integral part of the mental health evaluation process. Although somewhat subjective and imperfect, standardized clinical interviews (SCI) are currently considered to be the most reliable and valid methods available to screen for PDs. However, SCIs can be time consuming. While self-report instruments can be effective regarding efficiency and time conservation, the drawbacks are that a self-report inventory may have relatively poor specificity (bereft of elaboration from a clinician), the patients must possess, at minimum, a fundamental reading level, and the possibility of patient fatigue due to having to read and concentrate during the self-report assessment. The authors of this article suggest a compromise between an SCI and a self-report assessment in the form of a brief structured interview.

 

For this study, the authors employed the Quick Personality Assessment Schedule (PAS-Q), which is a brief structured interview that takes approximately 15 minutes to complete. The PAS-Q begins with open questions regarding character traits, personality traits, interpersonal relationships, occupational performance, substance use issues, and legal history. The next area, comprised of eight general sections, assesses constructs relevant to PDs: 1) Suspiciousness & Sensitivity (Paranoid PD); 2) Aggression & Callousness (Antisocial PD); 3) Aloofness & Eccentricity (Schizoid PD); 4) Impulsive & Borderline (Borderline PD); 5) Childishness & Lability (Histrionic PD); 6) Conscientiousness & Rigidity (Obsessive Compulsive PD); 7) Anxiousness & Shyness (Avoidant PD); and, 8) Resourcefulness & Vulnerability (Dependent PD). In order to identify a PD each section begins with two screening questions; positive responses to these screening questions leads to additional exploratory questions probing for PD symptoms, leading to scoring the characteristics in question. The intervieweer not only uses the information obtained from the PAS-Q, but also relevant historical/background information from the patient. The PAS-Q is scored according to four levels of severity ranging from 0 to 3: 0 = no severity; 1 = personality difficulty; 2 = simple PD; and, 3 = diffuse or complex PD.

 

The present study focuses on the validity of the PAS-Q. The purpose of examining the PAS-Q was derived from the following considerations: 1) the PAS-Q is based on the universally accepted ICD-10 categories (as opposed to the majority of the available PD screeners, which are predominantly based on the DSM classification system); 2) the PAS-Q does not focus on the prediction of any PD (as the majority of PD screening instruments do), but provides the opportunity to obtain more specific prognoses of distinct PDs; and, 3) the PAS-Q response scales are not limited to a simple dichotomy (i.e., absence or presence of PD symptoms) but instead allow for increased nuances corresponding with level of severity. The researchers chose the Structured Clinical Interview for DSM-IV – II (SCID-II) to serve as the basis of comparison as it is internationally the most widely use and best known measure to assess for PDs (the SCID-I examines Axis I Disorders, while the SCID-II examines Axis II disorders–which includes PDs).

 

Materials and Methods

 

For this study, the researchers randomly recruited 207 participants from a large community mental health center in the city of Tilburg, the Netherlands. However, 12 participants dropped out during the study. Of the 195 participants that completed the study, 112 were female (57.4 %) and 83 were male (42.6 %). The mean age of the participants was 32.7 years. The researchers utilized both the PAS-Q and the SCID-II in order to evaluate the participants. The PAS-Q was completed first; subsequently the SCID-II was completed 1-2 weeks later. The PAS-Q was then completed a second time 2-3 weeks later. The same clinician evaluated all the participants in order to eliminate extraneous variables regarding evaluator differences. The test-retest reliability of each item on the PAS-Q, in addition to the overall score, was estimated using Pearson correlation coefficients. The dimensionality of the PAS-Q was assessed using factor analysis. The effect of changes in the cut-off score of the PAS-Q for the purpose of predicting SCID-II diagnoses were assessed using receiver operating characteristic (ROC) analysis.

 

Results

Although the study began with 207 participants, 12 dropped out, resulting in 195 participants that completed the study.  Based on the SCID-II, a total of 97 of the 195 (50 %) participants received a PD diagnosis. In the group of participants with PD, the mean number of PDs was 1.8. The test-retest coefficient for the total score yielded a high score of 0.92. The section of Aloofness & Eccentricity had the lowest stability; the sections of Aggression & Callousness, Borderline, and Childishness & Lability had the highest stability over time.  Overall internal consistency, as reflected by Cronbach alpha coefficient, for the total PAS-Q scale was 0.35. Internal consistency coefficients were low, ranging from 0.16 (Borderline) to 0.47 (Conscientiousness & Rigidity). These scores are suggestive that a high degree of heterogeneity exists between the different sections. The scores of the factor analysis were as follows: 0.43 (regarding the positive connections between Aggression and Impulsiveness & Borderline), 0.50 (regarding Resourcefulness & Vulnerability and Anxiousness & Shyness), and 0.40 (regarding Aloofness & Eccentricity and Suspiciousness and Sensitivity). The ROC analysis was used to determine the effect of the changing cut-off score on the PAS-Q in predicting a SCID-II PD diagnosis. The ROC scores, as demonstrated graphically by a curve (the ROC curve), had an area-under-the-curve of 83 % (with a 95 % confidence interval). This is stating that the cut-off score correctly identified 81 % of the participant pool as correctly having a PD.

 

Discussion:

In 81 % of the cases the PAS-Q was able to correctly identify the presence of a PD. The researchers state that its low overall consistency should not be interpreted that the PAS-Q is a test that performs poorly. The researchers suggest that latent variables between the sets of items may be implicated in the low homogeneity of the sections. Overall, the researchers were pleased with the outcome of the PAS-Q, believing that it can be a useful tool to identify PDs in adult psychiatry. They suggest that patients that receive a score of 2 (or higher) should be interviewed detailed structured, or semi-structured, interview for PDs.

 

A perceived limitation of the applicability of the study (regarding use in the United States) is that the PAS-Q only assessed for 8 of the recognized 10 personality disorders from the DSM classification system. Although this is not considered a limitation of the study itself, since an objective of the study was to assess the validity of an instrument grounded in the ICD-10 classification system (and it accounts for the eight primary PDs recognized by the ICD-10). The authors also did not disclose the success rate of the comparative method, the SCID-II. The data regarding which of the participants had a PD was already obtained as all of the participants were preexisting members of the community mental health agency. Therefore the success rate of 81 % from the PAS-Q was held against the prerecorded diagnoses of the patients from the mental health clinic. The article did not mention the success rate of the SCID-II (unless it was to be assumed that the SCID-II had a success rate of 100 % since that was, presumably, the method in which the mental health clinic obtained their diagnoses in the first place). Finally, while the fact that a single interviewer conducted all the interviews is considered a strength of the study, it can also simultaneously be considered a weakness due to time constraints. The clinician conducted all the interviews was forced to conduct a high number of interviews in a relatively low amount of time, therefore some interviews may have been rushed, in addition to the fact that the participants’ background information was not reviewed for any of the cases.

 

Germans, S., Van Heck, G., Hodiamont, P. (2011). Quick Personality Assessment Schedule (PAS-Q): Validation of a brief screening test for personality disorders in a population of psychiatric outpatients.

Australian and New Zealand Journal of Psychiatry, 45, 9, p 756-762

 

 

With Thanksgiving behind us and the next holiday season coming up, many of us would like to avoid the extra pounds of holiday feasts! Psychcentral.com provided “5 Simple Steps to Avoid Overeating this Holiday Season.”

 

Acknowledging that most of us ignore our willpower over the holiday season, they created simple steps to help us make better choices with our food this holiday season.

 

These steps include:

 

1.  “Look at the food that is tempting you.” The author stated that looking at the food and recognizing that eating it is our choice is step number 1.

 

2.  “Imagine eating it.” He said that it’s okay to let your mouth water as you imagine eating and tasting the food, but make sure you keep going down these steps!

 

3.   “Now, imagine the food going down your throat and into your gut, where it will sit for the next several hours.” That thought might ruin the mouth watering! The author says to think about how your energy level will be and what your stomach will feel like after eating the food.

 

4.   “Ask yourself the question, “Do I want to feel how this food will make me feel?” Many of us struggle with mindless eating. We eat without thinking, which allows us to eat foods we wouldn’t normally eat and eat more than we would like to.

 

5.  “Make a choice.” If the answer to question 4 is “Yes” then go ahead! If the answer to question 4 is “No” it’s time to walk away.

 

The author stated that the purpose of this activity is to anticipate the feelings before you even eat the food. He wants us to think with our whole body (mind, stomach, taste buds) rather than just our taste buds.

 

He also highlighted that “self-sabotage” can be an issue for people and recommended this video to understanding self-sabotage and helping stop it!

 

Bundrant, M. (December 8, 2014). 5 Simple Steps to Avoid Overeating this Holiday Season. PsychCentral.com.

 

Brittany Best
WKPIC Doctoral Intern