Modeling Mental Health Recovery

Using a Hierarchical Linear Growth Model Karen Traxler, M.S., Suzy Landram, M.S., Tyler Kincaid, M.S., & Lisa Rue, Ph.D.

Applied Statistics and Research Methods

University of Northern Colorado

ASA: Women In Statistics; Raleigh Durham, North Carolina: May 15-17, 2014

Abstract Results & Discussion Methods

Purpose

Participants

All participants were residents of CooperRiis Healing Farm in North Carolina

Demographics Waves of Data

Measuring Recovery • The Mental Health Recovery Measure-Revised (Young and Ensing, 2003) is a 30-item self-report survey used to

measure recovery outcomes across five domains: overcoming stuckness , self-empowerment, learning and self-

redefinition, basic functioning, and overall wellbeing. A five-point traditional Likert scale is used to measure

responses from 1= strongly disagree to 5= strongly agree. Higher scores indicate more of the trait of recovery.

The MHRM-R is also used as a unidimensional measure of recovery where the scores are aggregated across all

30 items and can range between 30 and 150. Cronbach’s alpha for the global scale = .941 and RMSEA = .084.

Data Analysis: 5 waves of data • Using HLM 7 and SPSS (PASW, 22.0, 2013), only cases with no missing data, a two-level Hierarchical Linear Growth

Curve Model (HLM) was used to assess mental health recovery over time. Five time periods (or waves of data)

were used: admission, 3 months, 6 months, 9 months, and 12 months.

• The strength of a HLM Growth Curve Model over other regression models is that it can differentiate between

individual starting points (intercepts) as well as individual change/recovery over time (slopes) (Raudenbush &

Bryk, 2002).

Procedures • Grand mean centering was applied to an individual’s MHRM-R scores at each wave point to simplify the

interpretation of scores

• Maximum Likelihood Estimation (MLE) was used for all analysis

Building the Model Level-1 Model: The unconditional Model

MHRMRti = π0i + π1i*(TIMEti) + eti

Level-2 Model: The Conditional Model

π0i = β00 + β01*(ANXIETYi) + β02*(BIPOLARi) + β03*(DEPRESSIi) + β04*(SCHIZOPHi)

+ β05*(PERSONALi) + β06*(AGEi) + β07*(GENDERi) + r0i

π1i = β10 + r1i

Based on the RCC approach to mental health recovery, the purpose

of this study was to:

(a) Assess whether mental health recovery outcomes, based on the

MHRM-R improved over time for the target population

(b) Investigate the roles age, gender, and/or primary diagnosis play

in positive mental health recovery outcomes over time

Target Population: Individuals with Severe and Persistent

Mental Health Challenges

Statistically modeling mental health recovery for individuals with severe

and persistent mental health challenges has traditionally been

accomplished using the medical model of recovery which encompasses

the elimination or reduction of symptoms through medication and/or

hospitalization. A more holistic approach gaining support among both

clinicians and consumers is the Recovery-Centered Collaborative

Approach to mental health recovery (the RCC model) which is a

person-centered approach integrating medication with spirituality,

hope, physical wellbeing, life skills, strategies for managing symptoms,

and strong community and family support. Methods: Data from

CooperRiis Healing Farm, a residential treatment facility for individuals

with severe and persistent mental health conditions, specializing in the

RCC approach were examined using a Hierarchical Linear Growth

Model (HLGM) to assess recovery over a twelve month time period.

Data included variables such as age upon admission, gender, and

primary diagnosis. Results: The results from level 1 of the HLGM

provided evidence that there was significant positive growth in

recovery scores over time. Results from level 2 of the HLGM revealed

that individuals with a diagnosis of personality disorder or depression

had admission scores significantly lower than any other diagnoses.

Variance in growth over time was not explained by any of the level 2

variables

Special thanks to:

Dr. Sharon Young &

Matt Snyder, M.S., M.A., L.P.C.

CooperRiis Healing Community

Special thanks to:

Dr. Susan Hutchinson

University of Northern Colorado

Research Questions

• Interactions: Interactions of independent variables were tested and no significance

was found

• Proportion of Variance Explained: The primary diagnosis of the residents explains

35.3% of the parameter variance in the initial status (i.e., where a given resident

baseline recovery score will start) Future Research: Variance in growth over time has

not been explained by any level 2 variables in the model.

• Personality and Depression

Scores over time

Implications for Mental Health Researchers, Clinicians, and Consumers

• This study supports the holistic recovery-centered collaborative (RCC_ approach to

recovery as a viable alternative to the medical model of mental health recovery,

even for patients with the most severe and persistent mental health disorders

• Applied researchers and clinicians can use this information to develop appropriate

person-centered treatments for severe mental health conditions

• Individuals seeking mental health treatment for acute symptoms can have a voice in

their recovery process and maintain hope throughout their journey of symptom

management.

Limitations

Recovery is a complex process involving far more than age, gender, and primary

diagnosis, therefore significant limitations are inherent in any explanatory model of

recovery over time

• Research Question # 1: Does mental health recovery of individuals with

severe and persistent mental health conditions, receiving a recovery-

centered collaborative mental health intervention, improve over time?

o Is there a difference in recovery outcomes over time based on:

• Research Question # 2: gender?

• Research Question # 3: primary diagnosis?

• Research Question # 4: age?

All statistical tests were conducted with α =.05.

• Research Question # 1: Evidence supported growth (recovery) over time

Level 1: Unconditional Model:

This model only included the MHRM-R recovery scores as the outcome variable and TIME as an independent variable.

The unconditional model determined that there was indeed growth over time, allowing the addition of Level 2 to the

model, where possible explanatory variables were included (Raudenbush & Bryk, 2002).

Level 1 Intercepts: Estimations of the mean (pooled) intercepts, 𝛽 00= 105.88, p < .0001, were significant indicating

significant differences in scores on the MHRM-R at admission (baseline)

Level 1 Mean Growth Trajectories: The mean growth rate, 𝛽 10=21.21, p < .0001, for the MHRM-R recovery scores was

significant, providing evidence that residents were gaining an average of 21.21 points over the 12 months of recorded

MHRM-R recovery scores

(1)

Level 2: Conditional Model: See Table 3

Findings from the unconditional model confirmed the requisite of a conditional model

with explanatory variables of personal characteristics (i.e., age, gender, and primary

diagnosis) being added to the model, at Level 2:

• Research Question # 2: There was not a significant difference in the growth

trajectory of recovery outcomes based on gender (𝐵 11 = −1.49, 𝑝 = .724).

• Research Question # 3: Baseline Scores: Scores on the MHRM-R differed

significantly based on primary diagnosis; individuals presenting with depression

(𝐵 01 = −9.51, 𝑝 = .026) or personality disorders (𝐵 02 = −9.58 , 𝑝 = .046) scored

significantly lower upon admission than those with other diagnoses regardless of

gender or age of individual

• Research Question # 4: :There was not a significant difference in recovery

outcomes based on the residents’ age for the intercept (i.e., where the residents’

started; p = 0.620, nor in their growth trajectories (i.e., the residents’ recovery over

time; p = 0.358)

Implications & Limitations

(2)

Personality Disorders in red Scores upon admission differed

significantly based on primary diagnosis

Table 1

Years N Gender Primary Diagnosis n

2003-2013 285 Anxiety 5 Range 18-71

Bipolar 13 Mean 31.17

Depression 6 SD 11.60

Schizophrenia 97

Personality Disorder 23

Anxiety 12

Bipolar 20

Depression 8

Schizophrenia 39

Personality Disorder 15

Participants' Descriptive Statistics

Age (in years)

Male

Female

The MHRM-R is a self report

measure, and while results show

significant recovery scores, they

are based on the treatment

received at CooperRiis and may

have only moderate external

validity

[a] [b]

The growth model was based on 12

months of data. Trajectories may or may

not have improved with additional waves

[c]

Some individuals completed the

MHRM-R up to one month following

admission and, therefore, experienced

the benefits of treatment prior to their

first assessment

Depression

in red

Results & Discussion

Table 2

Wave of Data Time Period N

1 Admission 285

2 3 months 285

3 6 months 285

4 9 months 285

5 12 months 285

Time Periods of Data

Collection with Corresponding

Sample Sizes

Table 3

 Standard  Approx.

error d.f.

    INTRCPT2, β 00 105.8855 1.795575 58.970 281 <0.001

  DEPRESSION, β 01 -9.5800 2.154347 0.851 281 0.046

    PERSONALITY, β 02 -9.5100 3.2144 -1.631 281 0.026

     AGE, β 03 -0.0913 0.091555 -0.997 281 0.319

     GENDER, β 04 -1.4119 2.042749 -0.691 281 0.490

    INTRCPT2, β 10 21.21 2.277318 9.316 284 <0.001

Final Estimation of Fixed Effects in the HLM Model

Fixed Effect  Coefficient  t -ratio p-value

For INTRCPT1, π 0

For TIME slope, π 1