Predicting Initial Aftercare Appointment Adherence and Rehospitalization For

Predicting Initial Aftercare Appointment Adherence and Rehospitalization for

Individuals with Serious Mental Illness Discharged from an Acute Inpatient Stay

A Thesis

Submitted to the Faculty

Drexel University

Petra Kottsieper

in partial fulfillment of the

requirements for the degree

Doctor of Philosophy

May 2006

Acknowledgments

Completing this project is the final chapter of a dream and goal that I could not have reached without the help, support, and encouragement from many people. I would like to first thank Dr. Kirk Heilbrun, for his knowledge, mentorship, and feedback during not only the dissertation process, but my entire graduate career. I also would like to thank my committee members, Drs. James Herbert, Michael Lowe, Mark Salzer, and

Jeffrey Draine for their thoughtful contributions to this project. I want to especially thank

Drs. Draine and Salzer, for their continued mentorship and support throughout my graduate training.

I am very grateful for the time and effort of the following graduate and undergraduate students without whom I could not have completed this project: Samantha

Corrato, Jacey Erickson, Greg Kramer, and especially Rachel Kalbeitzer, who held it all together. I am grateful to the PMCU; its director Carol Boylan, and the staff who all welcomed the research project onto the unit. I’m also grateful to Michael Lewis at CBH for helping to make the CBH data available to me. Thank you all so much for your interest and commitment to this project. I would also like to thank Dr. Robert Archer at

EVMS for his interest and support during the final stages of this project.

Finally, I wanted to say thank you to my family, and my friends. To Brendan and the Keenan’s, your encouragement and support was vital in my pursuit of my dream in the first place. Thank you to all of you who helped me get through my first year of graduate school during a very difficult time in my life. To my dearest friends, thank you for your friendships and being understanding when I had no time, needed encouragement, and for all the motivation I received. To my mom, Christel, dad, step-dad, and my sister,

iii

Silke, thank you for your love and encouragement from across the ocean. To

Tony (aka measles), thank you for your love, support, laughter, taking care of the doggles, and just about everything else. Lastly, thanks to Hazel and Daisy, who often told me that they thought of maybe going back to college too, before once again drifting off to sleep nuzzled against me.

Table of Contents

LIST OF TABLES ...... viii

LIST OF FIGURES ...... ix

ABSTRACT...... x

1. INTRODUCTION ...... 1

1.1 The Importance of Treatment Adherence ...... 2

1.2 The Relationship between Medication Adherence and Appointment Adherence ...... 4

1.3 The Scope of Adherence/Non-Adherence...... 5

1.3.1 Psychiatric Medication Adherence ...... 5

1.3.2 Non-adherence Rates: Initial Psychiatric Appointment ...... 7

1.3.3 Non-adherence Rates: Ongoing Psychiatric Clinical Appointments ...... 8

1.3.4 Non-adherence Rates: Aftercare following Hospital Discharge ...... 9

1.4 Models of Medical/Health Decision Making ...... 12

1.4.1 Overview...... 12

1.4.2 Rational for HBM and TTM...... 14

1.4.3 Health Belief Model...... 15

1.4.4 Transtheoretical Model of Change...... 17

1.5 Factors Affecting Adherence and Non-Adherence in Individuals With Mental Illness or Co-occurring Disorders ...... 19

1.6 Empirical Evidence on Clinical Appointment Adherence and Medication Adherence ...... 21

1.6.1 Patient Characteristics ...... 21

1.6.1.1. Individual Static Characteristics ...... 21

1.6.1.2. Individual Dynamic Characteristics and the Health Belief Model...... 23

1.6.1.3. Individual Dynamic Characteristics, Motivation, and TMM ...... 27

1.6.2 Social Factors ...... 30

1.6.3 Illness/Clinical Determinants...... 32

1.6.3.1. Diagnosis, Symptom Severity, and other features of the Illness...... 32

1.6.3.2. Characteristics of the Treatment Regimen...... 33

1.6.4 Institutional and Cultural Determinants...... 35

1.6.5 Provider Determinants ...... 37

1.6.5.1. Clinical Setting...... 37

1.6.5.2. The Relationship between Health Care Provider and Patient...... 39

1.7 The Measurement of Treatment Adherence ...... 39

1.8 Aims of the Current Study ...... 41

2. METHODS ...... 43

2.1 Participants ...... 43

2.2 Procedure ...... 45

2.3 Measures ...... 48

2.4 Outcome Measures...... 54

2.5 Analytic Strategies and Statistical Methods ...... 56

2.5.1 Power Analysis ...... 5

2.5.2 Statistical Methods...... 57

3. RESULTS ...... 60

3.1 General Background Information ...... 60

3.2 Background Characteristics Rehospitalization ...... 60

3.2.1 Insurance ...... 61

3.2.2 Number of Previous Psychiatric Hospitalizations ...... 61

3.2.3 Mental Health Diagnosis at Discharge ...... 61

3.3 Background Characteristics Aftercare Adherence ...... 62

3.3.1 Case Management Services ...... 63

3.3.2 Length of Index Hospitalization ...... 63

3.4 Health Beliefs, Rehospitalization, and Aftercare Adherence ...... 63

3.5 Motivational Beliefs, Rehospitalization, and Aftercare Adherence ...... 65

3.6 Mutivariate Analyses ...... 65

3.6.1 Missing Data ...... 65

3.6.2 Predicting Aftercare Adherence...... 66

3.6.3 Predicting Rehospitalization ...... 73

4. DISCUSSION ...... 79

4.1 Aftercare Adherence...... 79

4.1.1 Risk Factors ...... 80

4.1.2 Motivational Variables ...... 83

4.1.3 Health Belief Variables...... 86

4.2 Clinical Implications...... 91

4.3 Hospital Recidivism ...... 9

vii

4.3.1 Risk Factors ...... 93

4.3.2 Motivational Variables...... 95

4.3.3 Health Belief Variables...... 95

4.4 Clinical Implications ...... 98

4.5 Limitations...... 99

4.6 Directions for Future Research...... 102

4.7 Conclusions...... 107

LIST OF REFERENCES ...... 110

APPENDIX A: ILLUSTRATION OF THE HEALTH BELIEF MODEL ...... 130

APPENDIX B: PARTICIPANT RECRUITMENT FLOWSHEET...... 131

APPENDIX C: COPIES OF MEASURES...... 132

APPENDIX D: INTERVIEWER QUESTIONNAIRE – FOLLOW UP ...... 147

APPENDIX E: RESULTS...... 148

APPENDIX F: CLASSIFICATION PLOTS LOGIST REGRESSION

ANALYSES ...... 165

VITA ...... 170

viii

List of Tables

1. Copies of Measures...... 132

2. Means, Standard Deviations, and Ranges for Sociodemographic, Health Belief Model, and Motivational Variables...... 148

3. Sociodemographic, Clinical, and System Variables of Participants Rehospitalized and Not Rehospitalized ...... 151

4. Sociodemographic, Clinical, and System Variables of Participants Aftercare Adherence Versus No Adherence ...... 153

5. Health Beliefs and Motivational Variables of Discharged Psychiatric Patients by Rehospitalization Versus No Rehospitalization...... 155

6. Health Belief and Motivational Variables of Discharged Psychiatric Patients by Initial Outpatient Mental Health Adherence Versus No Adherence ...... 156

7. Regression Analysis of Effects of Sociodemographic, Clinical Risk Factors, and Motivational Variables on Discharged Psychiatric Patients by Initial Aftercare Adherence...... 157

8. Logistic Regression Model Statistics for Discharged Psychiatric Patients by Initial Aftercare Adherence...... 160

9. Logistic Regression Analysis of Effects of Sociodemographic, Clinical Risk Factors, and Health Belief Variables on Discharged Psychiatric Patients Hospital Recidivism ...... 161

10. Logistic Regression Model Predicting Rehospitalization ...... 164

List of Figures

1. Illustration of the Health Belief Model...... 130

2. Participant Recruitment Flow Sheet ...... 131

3. Classification Model Step One: Socio-demographic, Clinical Variables and Aftercare ...... 165

4. Classification Full Model: All Variables and Aftercare ...... 166

5. Classification Model Step One: Socio-demographic, Clinical Variables and Hospital Recidivism...... 167

6. Classification Model Step Two: Risk and HBM Variables for Hospital Recidivism ...... 168

7. Classification Model Step Four: Full Model for Hospital Recidivism...... 169

Abstract Predicting Initial Aftercare Appointment Adherence and Rehospitalization for Individuals with Serious Mental Illness Discharged from an Acute Inpatient Stay Petra Kottsieper, M.Ed., M.S. Kirk Heilbrun, Ph.D.

Aftercare nonadherence and rehospitalization of individuals with serious mental illnesses has personal, economic, and clinical costs. Seventy-four participants were recruited from a hospital-based psychiatric unit to investigate factors associated with initial aftercare nonadherence, and rehospitalization in a 3-month post-discharge follow-up period. In addition to demographic, clinical, and system risk factors, this research used the Health

Belief Model (HBM) and the Transtheoretical Model of Change (TTM) as theoretical frameworks to predict health-care decision making. Risk variables were abstracted from participants’ charts. Prior to discharge, each participant completed questionnaires that were selected from the literature to approximate the constructs of the HBM, the TTM, and internalized and externalized motivation. Aftercare service contacts and rehospitalization data were obtained from the local behavioral health entity. Two separate logistic regression analyses were conducted to establish which model best accounted for the two outcomes. Approximately 58% of participants did not have an aftercare service contact in the 3-month follow-up period. Of the risk factors entered on the first step of a sequential logistic regression analysis, case management services significantly increased the odds of aftercare contact. Neither the variables testing the HBM nor the motivational constructs significantly added to model improvement. Rehospitalization data indicated that approximately 27% of participants were rehospitalized at least once in the 3-month follow-up period. Logistic regression analyses showed that the variables testing the

HBM significantly improved a risk factor model. Motivational variables did not add to the model. Participants with more favorable attitudes toward psychiatric medications

(measured with the DAI-10) had significantly reduced odds of rehospitalization, holding all other variables constant. Implications, study limitations, and future directions are discussed.

CHAPTER 1: INTRODUCTION

Treatment adherence and continuity of care for individuals with serious mental health problems are important public health issues. Nonadherence to medication regimens or appointments is associated with significant clinical, personal, and economic costs (McDonald, Garg, & Haynes, 2002; Perkins, 2002; Sirely, Bruce, Alexopoulos,

Perlick, Freidman, & Meyers, 2001). While adherence and nonadherence to public health care recommendations were first studied for tuberculosis in the late 1950’s (Hochbaum,

1958; Rosenstock, 1960), systematic evaluation of patients’ health care behavior began in earnest in the 1970’s (Turk & Meichenbaum, 1988). The confluence of expanded scientific knowledge, increasingly sophisticated treatment regimens, and pharmacological advances have been identified as pivotal in the development of programmatic research to study treatment compliance (Turk & Meichenbaum, 1988). Several researchers have argued that the term compliance implied a passive role for patients in their relationship with health care providers, and further suggested that the patient was solely at fault when recommendations or regimens were not followed (Barofsky, 1978; Fawcett, 1995;

Perkins & Pepper, 1999; Stimson 1974).

Substituting the term “adherence” was thought to change the paternalistic underpinnings of the construct of compliance, and in turn to emphasize an active role for the patient and an increased collaborative relationship between the patient and his/her health care provider. Treatment adherence has been defined as “a more active, voluntary collaborative involvement of the patient in a mutually acceptable course of behavior to produce a desired preventative or therapeutic result” (Meichenbaum & Turk, 1987, p.20).

For the purposes of this study, the term treatment adherence will be used. This term is currently widely employed, and is generally agreed to be less stigmatizing to the individual. Finally, it takes into account that the behavior is multiply determined.

The Importance of Treatment Adherence

In their book on facilitating treatment adherence, Meichenbaum and Turk (1987) give an overview of various examples of treatment nonadherence. Treatment nonadherence can refer to following medication regimens, keeping appointments, and following post-discharge aftercare recommendations and physician/public health recommendations. Across these different types of adherence, the most typical nonadherence rates have been estimated to range from 30%-60% (Masek, 1982).

Treatment adherence has also been identified as a major concern in the mental health and substance abuse fields (Campbell, Staley, & Matas, 1991; Centorrino, Drago-

Ferrante, Rendall, Apicella, Laengar et al., 2001). The nonadherence rates for keeping psychiatric appointments have been estimated to range from 15% – 75% (Rosenberg &

Raynes, 1976). Numerous studies have shown that unsuccessful treatment engagement of patients with mental disorders and/or substance abuse disorders have been linked to such clinical and personal costs as relapse, reduction in quality of life, and rehospitalization (Axelrod & Wetzler, 1989; Drake, Osher, & Wallach,1989; Green,

1988; Nelson, Maruish, & Axler, 2000; Olfson, Mechanic, Hansel, Boyer, Walkup, &

Weiden, 2000; Rosenfield, Caton, Nachumi, & Robbins,1986; Stanislav, Sommi, &

Watson, 1992; Stickney, Hall, & Gardner, 1980; Winston, Pardes, & Papernik, 1977).

The economic cost associated with treatment nonadherence has been estimated at $750 million a year for individuals with schizophrenia (Weiden, Olfson, & Essock, 1997), and

3 been identified as an increasing problem in the context of managed care when appointments are missed without cancellation, resulting in long waiting lists, wasted staff resources, and staff frustration (Campbell, Staley, & Matas, 1991; Grunebaum, Luber,

Callahan, Leon, Olfson, & Portera, 1996; Zweben & Zuckoff, 2002). Medication adherence rates and appointment keeping have been found to be lower for psychiatric illnesses than physical illnesses (Fenton, Blyler, & Heinssen, 1997; McDonald, Garg, &

Haynes, 2002; but cf. Blackwell, 1997). Less incongruous are findings that individuals with dual diagnoses such as serious mental illness disorders and co-occurring drug or alcohol disorders have increased nonadherence rates relative to individuals diagnosed with either a mental health disorder or substance use disorder alone (Carey, 1996; Drake,

Mercer-MacFadden, Mueser, McHugo, & Bond, 1998; Dubinsky, 1986; Wolpe, Gorton,

Serota, & Sanford, 1993).

In the context of antisocial behavior, research has indicated that individuals with serious mental health problems and/or substance use disorders have increasingly come into contact with the criminal justice system (Teplin, 1984). This has resulted in the development of jail diversion programs, which attempt to reconnect these individuals with treatment systems to prevent symptom exacerbation, rehospitalization, and/or further criminal justice contacts (Draine & Solomon, 1999; Steadman, Williams Deane,

Morrissey, Westcott, Salasin, & Shapiro, 1999). A study that specifically investigated nonadherence to psychotropic medications for a sample of dually diagnosed individuals found significantly elevated rates of violence for treatment non-adherent individuals

(Swartz, Swanson, Wagner, Burns, Hiday, & Borum, 1999).

Meichenbaum and Turk (1987) pointed out that treatment adherence varies according to the form of treatment prescribed or agreed upon. According to these investigators, the highest levels of adherence are observed in illnesses with acute onsets, and/or with treatments involving direct medications such as chemotherapy, injections, and high levels of supervision and monitoring. Lower adherence rates are seen in chronic disorders, especially where no immediate discomfort or risk is evident, for preventative health care such as mammograms and colorectal screenings, and when lifestyle changes

(such as following a diabetic diet) are needed. These finding also indicate that that lower aftercare treatment adherence is usual and expected in individuals with longer term mental health problems, especially after an acute exacerbation of symptoms has been treated.

The Relationship Between Medication Adherence and Appointment Adherence

Medication adherence and adherence to scheduled appointments are the two areas most often studied with regard to behavioral health problems. It has been hypothesized that medication nonadherence (or, more specifically, discontinuation of prescribed psychotropic medication) might be highly correlated with outpatient treatment nonadherence (Nose, Barbui, Gray, & Tansella, 2003; Weiden, Olfson, & Essock, 1997;

Zygmut, Olfson, Boyer, & Mechanic, 2002). If individuals do not (for whatever reason) take their prescribed medications, they might also fail to attend their appointments; individuals who are dissatisfied with the services they receive at their appointments might discontinue medication secondary to appointment nonadherence. However, a positive correlation between these two types of nonadherence would not reveal the temporal patterning of these two types of nonadherence. No empirical study could be located that

5 specifically addressed this issue. Research has not distinguished these separate categories of nonadherence, so nonadherence of either type may be conceptualized as a single variable (Nose et al., 2003). For the purpose of conceptual clarity, however, these two types of adherence will be discussed separately. The next section will overview different categories of treatment adherence and the empirical evidence associated with them.

The Scope of Adherence/Nonadherence

Psychiatric medication adherence. Medication nonadherence can take various forms. These can include never filling a prescription, filling it but never taking it, discontinuing it, taking it inconsistently, or not following the correct dosing instructions

(Perkins, 2002). A review of the literature on medication compliance (Cramer &

Rosenheck, 1998) confirmed that medication adherence is problematic in every area of medicine, whether related to physical or behavioral health problems. Cramer and

Rosenheck (1998) noted that psychiatric medication adherence was found to have a wider range and lower adherence rates than medication for non-psychiatric illnesses. For antipsychotic medication, their review yielded a mean adherence rate of 58%, with a range of 24%-90 %. For antidepressant medication, a mean adherence rate of 65%, with a range of 40%-90% was obtained. Pooling 12 studies for non-psychiatric disorders, the mean adherence rate was found to be 76%, with a range of 60%-92%. The authors cautioned, however, that these findings might have been confounded by the different types of adherence definitions, and adherence measurements used between the psychiatric and non-psychiatric studies compared for the review. In another review of medication adherence, Blackwell (1996) did not find differences between adherence to psychiatric versus non-psychiatric medications. Medication nonadherence rates for

6 individuals with schizophrenia were reported to be in the middle range of rates reported for common medical disorders such as arthritis or seizure disorders (Fenton, Blyler, &

Heinssen, 1997; Young, Zonana, & Shepler,1986).

Nonadherence has been especially scrutinized in the schizophrenia spectrum diagnosis, as nonadherence with prescribed psychotropic medication accounts for 40% -

55% of all psychiatric rehospitalizations and relapses into active illness states (Perkins,

2002; Weiden, Olfson, & Essock, 1997). Weiden and Glazer (1997) investigated reasons for rehospitalization to an acute inpatient unit for individuals with schizophrenia who already had several previous inpatient hospitalizations. Sixty-three of 131 screened admissions met their definition of a “revolving door” patient. The authors found that the most common reason for rehospitalization was nonadherence to prescribed medications, followed by non-responsiveness to medications. Medication nonadherence rates have been reported to increase when individuals with serious mental illnesses are also using either drugs or alcohol. One study reported that individuals diagnosed with schizophrenia and co-occurring substance abuse were 13 times more likely than non- substance abusing individuals to be non-adherent with antipsychotic medication

(Kashner, Rador, Rodell, Beck, Rodell, & Muller, 1991). Owen, Fisher, Booth, and

Cuffel (1996) found that substance abuse in the 30 days immediately prior to inpatient hospitalization was the strongest predictor of medication non-compliance at a 6- month follow-up. These authors also found that study participants who abused substances were

8 times more likely than non-substance abusing individuals to be non-adherent with antipsychotic medication, and that substance abuse interacted with decreased outpatient contact, resulting in poorer clinical outcome.

Nonadherence rates: Initial psychiatric appointment. The appointment adherence literature for individuals with psychiatric disorders and/or dual diagnosis has received less attention than research investigating medication adherence (Centorrino, Hernan,

Drago-Ferrante, Rendall, Apicella, Laengar et al., 2001). In one study, a medical school affiliated outpatient clinic treating individuals with serious mental illnesses reported that

36% of individuals did not attend their scheduled initial appointment (Kruse, Rohland, &

Wu, 2002). Investigators in a second study reported that in an urban hospital medical clinic, fully 50% of 180 consecutive physician referrals for psychiatric consultation missed their initial psychiatric consultation appointments (Grunebaum, Luber, Callahan,

Leon, Olfson, & Portera, 1996). Consistent with this, other investigators of attendance in an outpatient clinic for dually diagnosed individuals noted that 44% of individuals referred from a variety of sources stopped attending after 3 or fewer appointments

(Bogenschultz & Siegfried, 1998).

This percentage was only slightly lower in a study involving 1,106 consecutive patients who obtained an initial outpatient appointment at a medical center department of psychiatry during a 1-year period: 32% did not keep their initial scheduled appointment

(Carpenter, Morrow, Del Gaudio, & Ritzler, 1981). An interesting aspect of this study was the investigators’ telephone follow-up contact with almost 30% of those who did not keep their initial appointment. This follow-up revealed that of those contacted, 22% eventually reestablished contact with the clinic and 25% reported seeking other help for their mental health problems. This suggests that while nonadherence in initial appointments is a problem in terms of wasted staff resources and longer waiting times for appointments, it appears that at least 70% make initial contact and about half of the

8 individuals who initially did not attend eventually make contact to obtain mental health services.

Nonadherence rates: Ongoing psychiatric clinical appointments. Interest in facilitating appointment attendance at community mental health centers dates back to the late 1970’s and early 1980’s (Carr, 1985; Masnik, Olarte, & Rosen, 1981; Turner &

Vernon, 1976). The range in nonadherence rates for ongoing outpatient appointments varies according to strategies for promoting appointment adherence used by the mental health center, the type of services offered, the population served, and the location, among other factors (Gomez-Carrion, Swann, Kellert-Cecil, & Barber, 1993; Shivak & Sullivan,

1989).

In one review of 896 clinic visits of 62 psychiatric outpatients at four different specialty clinics over a 3-month period, only 83 (9.3 %) appointments were missed

(Centorrino et al. 2001). However, these investigators also reported that adherence rates varied greatly between at least two of their clinics. It appeared that different services

(verbal therapy versus medication management) and differing sanctions (billing for missed appointments) were provided and used by these two clinics. This finding stood in contrast to other data on outpatient treatment adherence, where nonadherence varied between 40% in the U.K. (Killaspsy, Banerjee, King, & Lloyd, 2000), 19.5% for an outpatient clinic located in a psychiatric hospital in Canada (Campbell et al., 1991), and

58.8% for an inner city neighborhood in the U.S. (Shivack & Sullivan, 1989). It has been observed that there are two types of treatment dropouts: those who do not return following an initial appointment, and those who drop out after the first appointment, but return within the first year (Chen, 1991). Rates have been reported to vary from 30% to

60% for individuals who drop out after attending at least a few of their appointments

(Sweet & Noones, 1989).

Nonadherence rates: Aftercare following hospital discharge. Rates of aftercare adherence have been related to the type of linkage between the inpatient and outpatient providers, the type of services offered, the population it serves, and geographic location

(Appleby, Luchins, Dyson, Fanning, & Freels, 2001; Boyer et al., 2000; Olfson,

Mechanic, Boyer, & Hansel, 1998; Sharma, Elkins, van Sickle, & Roberts, 1995;

Stickney, Hall, & Gardner, 1980). Stickney and colleagues (1980) reported that initial appointment follow-up ranged from 22%-75%, according to the referral/linkage procedure used by the providers. For patients who did not have a scheduled follow-up appointment prior to discharge, the adherence rate with an initial aftercare appointment was 22%. For individuals who met an aftercare nurse from the outpatient provider while still in the hospital, but were left to make their own appointments with the facility following discharge, the rate of initial aftercare adherence rose to 36%. For inpatients that were provided with a specific appointment time and individual to meet with following discharge, 68% attended their first scheduled appointment. The highest rate of initial adherence (75%) was obtained for a group of patients who met with a specific provider while still in the hospital and were provided with a scheduled aftercare appointment with the same provider. A secondary purpose of this study was to investigate rates of rehospitalization, depending on the different linkage strategies. The results mirrored the findings for aftercare attendance; the highest rehospitalization rate within one year following discharge from index hospitalization was 39%, and occurred in the group with no aftercare planning. The lowest rate (28%) occurred in the group that had

10 both a pre-discharge contact and a follow-up appointment. While the findings gave a good indication of the effect of different linkage options on aftercare adherence, such findings need to be interpreted with caution. The study did not have a randomized design, and assignment to the different referral systems was made on a consecutive basis, allowing other system changes to occur and possibly affect the results.

Olfson et al. (1998) conducted a longitudinal observational study at four general hospitals in New York City. For patients referred to a new provider, adherence rates varied from 63%-98 %. For individuals who returned to their previous outpatient providers following hospital discharge, the observed adherence rates were 73 % to 91 %.

It should be noted that of all patients referred to a new outpatient provider, 81% (43 patients) went for an interview at an outpatient program while they were still in the hospital, which probably accounts for the relatively high appointment adherence rates in this group.

Appleby et al. (2001) found that 42% of their discharged inpatients attended their first outpatient appointment. These authors also investigated two linkage strategies (in- person contacts versus telephone), and found that initial aftercare treatment adherence was significantly and independently predicted by predischarge, face-to-face contact with community representatives.

Boyer, McAlpine, Pottick, & Olfson (2000) investigated patient risk factors for not accepting or completing outpatient referrals and the effectiveness of several linkage strategies between inpatient and outpatient follow-up care. They found that about two- thirds (65%) failed to keep their initial outpatient appointment, even if it was rescheduled, with a nonadherence range of 37% to 80.8%. For about two-thirds of the

11 study group, hospital physicians discussed the discharge of a patient with an outpatient provider. In this group, 43% kept their initial outpatient appointment compared to 19% of patients where no such communication took place. A second linkage strategy, patient meeting with outpatient provider while still in the hospital, also had a significant impact on appointment keeping. For those patients, 47% kept their appointment (compared to

29% of those patients who did not have such a meeting). For patients who visited an outpatient program prior to discharge, 48% kept their appointment; for those who began such a program prior to discharge, 62% kept their post-discharge appointment. All of these strategies were significant at the p > 0.01 level in chi-square analysis, and increased the odds of attendance (odds ratios = 2.14-3.89). In a multivariate logistic regression, two strategies remained significant: (a) patients visiting the program before discharge, and (b) communication between inpatients and outpatient providers, after controlling for patient risk factors (odds ratios = 3.90 and 3.91, respectively).

A review by Nelson et al. (2000) indicated that of a total of 3,113 psychiatric admissions in eight southwestern states, 542 (17.4%) were re-admissions, and of these

136 (25%) kept at least one appointment after their first discharge, whereas 406 individuals (75%) did not. This affected re-admission rates differentially; the longer individuals went without an appointment in the 1-year study period, the more likely they were to be rehospitalized. This rate remained constant over time for individuals who had at least one follow-up outpatient appointment following index hospitalization. Compton,

Rudisch, Craw, Thompson, & Owens (2006) investigated practical, readily accessible patient risk factors in their retrospective study of missed first outpatient appointments in a community mental health setting after psychiatric hospitalization. These authors found

12 that of 221 discharged patients from two urban, county hospitals, 36% kept their first scheduled follow-up appointment, whereas 64% did not. Specific linkage strategies, such as reminder phone calls, were not studied as the authors reported that these strategies were not provided by the two hospitals.

Models of Medical/Health Decision Making Overview. Several authors have called for an increased use of theory and models to guide the study of nonadherence in the arena of behavioral health, as they have pointed out that most research in this area remains atheoretical (Klinkenberg & Calsyn, 1996;

Zygmut et al., 2002). Several theoretical models have been proposed, with some specific to health decision making and others to decision making more generally. These models attempt to provide a coherent framework for human healthcare decision making, including adherence. All models include constructs that can guide research through hypothesis testing and direct the provision of health care interventions.

One of the first models to specifically consider health service use was the

Behavioral Model of Health Service Use, developed by Andersen originally in the 1960’s

(BMHSU; Andersen, 1968; 1995). Originally this model was mainly concerned with access to health care. It considered individuals’ propensity to use care by focusing on demographic and economic considerations, the accessibility of resources, and their perceived need for care. Most other models of health decision making are either cognitive or social in their orientation. These models are described as cognitive when the primary focus is on an individual’s internally mediated behaviors, while social models attempt to capture dynamic environmental factors related to individuals’ decision making. There are several cognitive or social cognition models, including the various versions of the

Health Belief Model (HBM) (Becker, 1974; Hochbaum, 1958; Kirscht, 1974;

Rosenstock, 1960; 1974), the Theory of Reasoned Action (TRA) (Fishbein & Ajzen,

1975), the Theory of Planned Behavior (TPB) (Ajzen, 1985), the Protection Motivation

Theory (PMT) (Rogers, 1975), and the Transtheoretical Model of Change (TTM)

(Prochaska & DiClemente, 1983; Prochaska, DiClemente, & Norcross, 1992; Prochaska,

Redding, & Evers, 2002; Prochaska & Velicer, 1997). Models such as the Network-

Episode Model (Pescosolido, 1991, 1992, 1998; Pescosolido & Boyer, 1999) focus on an individual's dynamic interactions between their illness career and other individuals and organizations such as family members, peers, health care providers, health care systems, and specific organizations. The latter model conceptualizes health care decisions as primarily socially determined, and as dynamic. The interplay between the various factors creates a social network that is embedded in the larger cultural and social contexts in which a person’s decision-making occurs. In a study on aftercare compliance for individuals with dual diagnosis (Pollack, Stuebben, Kouzekanani, & Krajewski, 1998) consumers reported that a multitude of factors adversely affected their aftercare adherence. These factors were described by the authors as intrapersonal (family influence, perceived support, etc.), social (stigma, external controls, etc.), intrapersonal

(perceived benefits, medication effects, motivation, denial, etc.), and environmental

(resources, weather, accessibility). This study suggests that health care decision making is a complex and fluid process, and might be best understood by a combination of components of the above described theories.

Rationale for HBM and TTM. The following review and the current study will focus on two of the models, the Health Belief Model and the Transtheoretical Model of

Change. Ogden (2003) pointed out that social cognitive models provide a pragmatic framework for the development of interventions designed to change health related behaviors, and can be considered useful as a theory, as long as cognition and behavior are assessed distinctly. The Health Belief Model (HBM) was chosen as it is based firmly on social psychological theory and depends heavily on motivational and cognitive factors

(Katazky, 1977). It further continues to be the focus of considerable theoretical and research attention (Janz & Becker, 1984). It has utility in research on all of the specific categories of overt health behavior as proposed by Kasl and Cobb in their seminal articles

(1966a, 1966b), which the authors conceptualized as preventive health behavior, illness behavior, and sick role behavior.

The Transtheoretical Model of Change grew out of an attempt to integrate processes and principles of change from numerous theories of behavior change and psychotherapy (Prochaska, Redding, & Evers, 2002). As such, the model incorporates emotions, cognitions, and behavior. The Transtheoretical Model of Change has added to existing theories the concept of readiness for change, a dynamic construct that can change over time. The TTM is a theoretical model of behavior change which has been the basis for developing effective interventions to promote health behavior change. For example, the TTM has provided a framework for behavior change interventions such as motivational interviewing (MI) (Miller & Rollnick, 1991, 2002), which has been popular in substance abuse treatment. More recently, MI and its variations have also been utilized for several other types of behavior change and populations, such as smoking

15 cessations, weight loss, and treatment adherence (Burke, Arkowitz, & Menchola, 2003;

Swanson, Pantalon, & Cohen, 1999). The University of Rhode Island Change

Assessment (URICA), was developed to assess psychotherapy patients' readiness to address the "problem" (unspecified) that brought them to treatment (McConnaughy,

DiClemente, Prochaska, & Velicer, 1989; McConnaughy, Prochaska, & Velicer, 1983).

Given the current populatrity of the TTM, the existence of a psychometrically sound assessment instrument that measures the readiness for change construct of the TTM, and the increasing application of therapy approaches (such as motivational interviewing) that fit well into the TTM farmework, make the TTM an important model to investigate in the realm of mental health care behavior.

Health Belief Model (HBM). The HBM was one of the first models to investigate why people did not follow through with public health recommendations (Hochbaum,

1958; Rosenstock, 1960), and is one of the most heavily researched models of health- related behaviors, including treatment adherence (Janz & Becker, 1984). This model has most often been applied to physical health behaviors (Janz & Becker, 1984), although it has also been applied to behavioral health issues such as smoking and drinking (Sutton,

1987). More recently, HBM has also been applied to psychiatric medication adherence

(Adams & Scott, 2000; Cohen, Parikh, & Kennedy, 2000; Fenton, Blyler, & Heinssen,

1997; Scott, 2002), the influence of health beliefs on mental health service utilization

(Raybuck, 1998), and indirectly to predict service engagement for individuals with psychosis (Trait, Birchwood, & Trowler, 2003).

The HBM is based on expectancy-value formulations that posit that a given health care decision, such as adherence, is based on the relationship between the expected

16 probability of a given outcome and the subjective values or utilities attached to that outcome (Sutton, 1987). Since its inception, the model has been clarified and expanded to account for behavior such as sickness role behavior, preventative actions, utilization of health care resources, and self-efficacy (Janz, Champion, & Strecher, 2002). Originally the model consisted of four main constructs hypothesized to influence the likelihood of a given decision. See Appendix A (Figure 1) for an illustration of the Health Belief

Model.

The construct of perceived susceptibility refers to an individual's subjective perception of the risk of contracting an illness. For established conditions, this applies to the perceived risk of relapse, belief in the diagnosis, and feelings of susceptibility to illness. Perceived severity refers to feelings of the seriousness of the health problem in question. The person is thought to consider the medical/clinical consequences to herself, and the possible social consequences of the health condition. In combination, these two constructs are sometimes referred to as a person's threat perception.

The HBM also posits that an individual performs a behavioral cost-benefit analysis, and the outcome of this analysis is determined by an individual's thoughts and feelings about the perceived benefits versus perceived risks for a given health behavior.

Perceived benefits are a person's beliefs concerning how helpful and feasible the available treatment or health action might be. Perceived barriers refers to identifying and weighing the potential costs of a given health action, such as financial expenses, dangerousness, inconvenience, and others.

In addition to these original four constructs, other factors were thought to moderate and mediate individuals' health behaviors. Various demographic,

17 psychological, social, and structural variables are thought to affect individuals' perceptions and behavior (Janz, Champion, & Strecher, 2002). That health behavior is influenced by extra-personal variables such as reminders, public health campaigns, family support, or pressures has also been recognized. These social factors are called

Cues to Action, but they also refer to internal cues, such as symptom worsening or medication side effects.

It has been argued that the construct of self-efficacy (Bandura, 1977a) should be added as an additional variable to the HBM (Rosenstock, Strecher, & Becker, 1988).

Self-efficacy is a distinct construct encompassing an individual's beliefs that he can successfully execute the behavior required to produce a given outcome, especially when the required behavior is long-term (e.g., medication adherence or treatment adherence for a chronic condition) (Janz et al., 2002). Self-efficacy might be especially salient for those with prior inpatient hospitalizations who have failed at different times to obtain treatment services and/or remain medication adherent.

Transtheoretical Model of Change

Since its inception, this model has been extensively applied to behavioral health issues such as smoking cessation, drug abuse, alcohol abuse, eating disorders, dieting, gambling, obesity, exercise, condom use, and job seeking behaviors, to name but a few areas of investigation (Project MATCH, 1997; Prochaska, 1992; Prochaska, Redding, &

Evers, 2002). It has also been suggested as an ideal therapeutic model for individuals with dual diagnosis, as it provides a way to assess individuals’ readiness to accept treatment for the two disorders, can be used as a measure of clients’ progress through treatment, and can also guide the intervention selection to appropriately match clients’

18 needs and readiness (Brady, Hiam, Saemann, Humbert, Fleming, & Dawkins-Brickhouse,

1996; Carey, Carey, Maisto, & Purine, 2001; Daley & Zuckoff, 1999; DiClemente &

Scott, 1997). In the mental health and dual diagnosis domains, the model has been applied to clients’ progress in therapy (Lichner, 2002), and has improved treatment adherence through the use of motivational interviewing, a therapeutic intervention closely modeled on the Transtheoretical Model of Change (Daley, Salloum, Zuckoff, Kirisci, &

Thase, 1998; Daley & Zuckoff, 1999; Kemp, Hayward, Applewaithe, Everitt, & David,

1996; Kemp, Kirov, Everitt, Hayward, & David, 1998; Swanson, Pantalon, & Cohen,

1999).

In this model, behavior change is thought to occur when a person moves through a number of stages and completes varied and specific tasks at each stage. There are a total of five stages in this model. The first stage is called precontemplation, when a person is not considering behavior change. In the contemplation stage, behavior change is considered, but no active steps are taken toward this change. In this stage, ambivalence is assumed to be very high, as an individual considers and weighs the advantages and disadvantages of a given behavior change. The first active steps occur in the preparation stage, when a person might learn where an outpatient provider is located, with the intention to make an appointment soon--or the person has made a phone call or set up an intake interview. In the action stage, the individual takes concrete steps; an appointment is attended, psychiatric medication is refilled, medication is taken, and the like. The action stage is suggested to last about six months from the initiation of the first overt behavior change. The final stage in this model, maintenance, denotes an overt behavior change has been sustained for six months or more.

How people move from one stage to the next has been described in a comparative analysis of leading theories of behavior change and psychotherapy (Prochaseka et al.,

2002). Ten processes were identified describing the activities of individuals at different times in their efforts to change a particular behavior. What makes this model attractive, when compared to other theories of change, is the notion of behavior change as incremental in its application to a variety of behaviors and settings, the notion that individuals can be at different stages of change for different behaviors at the same time, and that interventions can be tailored to meet clients’ levels of readiness to consider or engage in change processes.

Factors Affecting Adherence and Nonadherence in Individuals with Mental

Illness or Co-occurring Disorders

In his influential work on treatment adherence for physical as well as behavioral health, Hayes (1976) identified about 200 variables that had been investigated relevant to adherence. These correlates have been grouped into several broad categories.

Meichenbaum and Turk (1987) identified the following: (a) characteristics of the patient,

(b) characteristics of the treatment regimen, (c) features of the disease, (d) relationship between the health care provider and the patient, and (e) clinical setting. Klinkenberg and Calsyn (1996) classified nonadherence predictor variables to psychiatric aftercare into three categories: (a) client vulnerability (background variables, psychiatric variables), (b) community support (living situation, informal social support), and (c) system responsiveness (medications services, making an appointment, outreach, and waiting lists). Gochman (1997) identified five categories of health behavior determinants related to treatment adherence: (a) personal, (b) social, (c) institutional, (d) cultural, and

(e) provider determinants. With the addition of a separate illness/clinical variable,

Gochman’s model will be used to present research on factors associated with appointment nonadherence.

Two separate types of inquiry will be reviewed in this section: research that has primarily focused on the prediction of treatment or medication adherence, and studies that have attempted to increase treatment or medication adherence by utilizing different types of interventions. Studies that have investigated variables that relate to the HBM or

TTM models are also reviewed in the adherence categories. While these categories are useful as organizational and conceptual tools, it should be noted that any number of these components can simultaneously affect health decision making behavior. For example, a study that investigated variables such as perceived stigma, perceived attitudes toward one’s own illness, and views about treatment (Sirey, Bruce, Alexopoulos, Perlick,

Freidman, & Meyers, 2001) describes an individual’s perception, and will therefore largely be located in the personal determinant category. However, this categorizing is not meant to imply that perceived barriers to treatment adherence such as stigma, for example, are not based in real discrimination. Hence, the finding that individuals with higher perceived stigma were less medication adherent could also be placed in the social or cultural section. Health care attitudes and beliefs are influenced by experiential, as well as economic and socio-cultural factors in a family, society or cultural group at a given time (Pescosolido, 1991, 1992, 1998, 1999; Snowden, 2001).

Empirical Evidence on Clinical Appointment Adherence and Medication Adherence

Patient Characteristics

The following description will distinguish between two main dimensions of personal characteristics. Borrowing from the violence risk literature, one kind of these variables can be called static. (These variables are largely unchangeable through planned intervention, such as a person’s gender, or race). The other kind can be called dynamic

(potentially changeable through planned intervention), including attitudes, knowledge, perceptions, and beliefs a person might espouse. While always embedded in larger socio- cultural contexts, and sometimes systematically related to some of the static factors

(Pescosolido & Boyer, 1999), dynamic factors are more relevant when considering the individual’s potential for change. For example, a psycho-educational intervention targeted at increasing medication compliance is aimed at increasing patients’ knowledge, or changing their beliefs about their illness and the prescribed medication, and hence may lead to a change in health behavior (Zygmut et al., 2002).

Individual static characteristics. Given that a majority of research on the prediction of treatment adherence has used retrospective designs, these studies have mainly focused on static variables such a socio-demographic, or system level variables.

Studies designed to identify the static correlates of treatment nonadherence such as race, gender, and age have found either no significant differences in socio-demographic variables, or inconsistent results (Sparr, Moffitt, & Ward, 1993; Carpenter et al., 1981;

Nicholson, 1994; Kruse, Rohland, & Wu, 2002; Killapsy et al., 2000).

While disparities in access to formal types of mental health care utilization have been documented (Snowden 2001; Hu, Snowden, Jerrell, & Nguyen, 1991), it also appears that

22 there are complex interactions of location, cultural barriers, cultural beliefs, stigma, limited financial resources, and coping styles for African American consumers, for example, underscoring the complexity of understanding this process (Snowden, 2001).

This might explain why using race as a predictor has lead to inconsistent outcomes.

The most consistent socio-demographic predictor for treatment nonadherence has been younger age (Carpenter et al., 1981; Nicholson, 1994; Kruse, Rohland, & Wu,

2002). In a study of referrals from a state agency to intake appointments at a university outpatient clinic, Kruse et al. (2002) found that the 36% of referred individuals who missed their appointments were significantly younger, more often Hispanic, and with a significantly poorer family support system than those who attended. However, a study focused on identifying risk factors of discharged inpatients to initial follow-up care

(Boyer et al., 2000) found that younger age was the only significant socio-demographic variable associated with increased first outpatient appointment adherence. It should be noted that the mean age of the adherent group was 39 years, whereas the mean age of the non-adherent group was 44 years. The clinical utility of this difference might be questionable, because nonadherence associated with younger age usually refers to individuals in their twenties who might be experiencing either their first illness episodes, and/or have not yet come to the attention of the mental health system. Boyer et al. (2000) also considered gender, race, education, and insurance status, but did not find that any of these patient characteristics distinguished those who attended their first appointment from those who did not. Killaspy et al. (2000) reported no significant differences in socio- demographic variables between their groups of treatment adherent and non-adherent.

Klinkenberg and Caslyn (1996) reviewed the literature on outpatient treatment adherence and recidivism following discharge from inpatient psychiatric hospitalization between 1974 and 1994. Their findings underscored that different socio-demographic characteristics were often significant in individual studies, but not generalizable and thus not broadly predictive of aftercare nonadherence.

Individual dynamic characteristics and the Health Belief Model. It has been suggested that factors such as motivation, beliefs, perceived stigma, treatment expectations, and treatment satisfaction are all related to adherence (Daley & Zuckhoff,

1999). These variables can all be subsumed into one of the different constructs of the

Health Belief Model (or, in the case of motivation, the TTM). However, while attitude research is central to the study of individuals’ behavior in the realm of social psychology, only a few studies have investigated individuals’ attitudes in relationship to treatment adherence, and the majority of investigations have centered on attitudes toward adherence to psychotropic medication regimens (Angermeyer, Daeumer, & Matschinger, 1993;

Croghan, Tomlin, Pescosolido, Schnittker, Martin, & Lubell, et al., 2003; Hoencamp,

Stevens, & Haffmans, 2002; Perkins, 2002).

In a study of 1,387 U.S. household volunteers (Croghan et al., 2003), most survey participants considered psychiatric medications to be effective, while less than 50% of the respondents were concerned about side effects. However, despite regarding psychiatric medications as efficacious, 51% of respondents nevertheless reported they would not be willing to take them. The authors note that willingness to take psychiatric medication is influenced by these attitudes and other factors, including health status and past use of mental health treatment. In a similar study in Germany, the results were even

24 more striking. In a survey of 3,098 adults in 1990, Angermeyer et al. (1993) found that only 33% of their sample reported that the use of psychotropic medications might be warranted for mental disorders, while 66% rejected their use. Arguments given by the respondents opposing the use of psychiatric medication fell into three categories: (a) medications have side effects and especially addiction potential, (b) medications do not address the “cause” of the illness but only the symptoms, and (c) medications are not efficacious, or only work for a brief period.

There is a sizeable literature that has investigated the utility of HBM constructs in guiding research on medication nonadherence (Adams & Scott, 2000; Connelly,

Davenport, & Nurnberger, 1982; Kelly, Maimon, & Scott, 1987; Nageotte, Sullivan,

Duan, & Camp, 1997; Scott, 2002; Scott & Pope, 2002). Adams and Scott (2000) studied perceived benefits of adherence (e.g., being symptom free), perceived barriers to adherence (e.g., stigma of taking medications, side effects), perceived susceptibility (e.g., belief that a relapse might occur), and perceived severity of outcome (e.g., belief that relapse might have negative consequences) in a sample of participants with affective disorders and schizophrenia. Perceived benefits of treatment and perceived severity of illness accounted for 43% of the variance in medication adherence behavior. However, the predictive utility of the HBM varied widely between these studies, from 19%-20%

(Connelly et al., 1982; Kelly et al., 1987) to 47% (Adams & Scott, 2000). Adams and

Scott speculated that using psychometrically sound instruments that more reliably measured the HBM constructs might have accounted for the results of his study. Having the belief that one has a mental illness was also found to be significantly associated with medication adherence by several other investigators (Nageotte et al., 1997; Scott & Pope,

2002; Ziguras, Klimidis, Lambert, & Jackson, 2001), and supports the value of the threat perception constructs of the HBM in accounting for medication adherence.

The few studies that have measured individuals' agreements with their health care providers’ recommendations have found that treatment adherence was higher when an individual agreed with a health provider’s recommendation (Killapsy et al., 2000;

Grunebaum, Luber, Callahan, Leon, Olfson, & Portera, 1996). An interesting feature of

Killapsy et al.'s (2000) study was that the two groups of non-adherent, newly referred and non-adherent, follow-up participants were followed over time, and given an opportunity to indicate why they had not kept their outpatient appointments/referrals. Being too psychiatrically unwell was given as a reason by 14% of both groups. A total of 11% of follow-up patients and 14 % of new patients indicated that the outpatient clinic made a clerical error with the appointment. The other reasons differed across the two groups.

The non-adherent, follow-up group endorsed forgetting (27%) more often than the newly- referred patients (11%), although this difference was not tested for statistical significance.

Nonadherence was explained by 17% of the newly referred patients because they did not agree with the referral, and 11% were afraid of a possible psychiatric inpatient admission, whereas no patients in the follow-up group cited either of these reasons for their respective nonadherence.

While the cited reason for missing appointments of being “too sick” might be inconsistent with the results of other studies reporting severity or acuteness of illness to be predictive of adherence, participants’ responses were qualitative, and apparently not further probed. It is therefore unclear what “being too sick” meant for respondents. In contrast, disagreement with a new referral can be interpreted as being related to an

26 individual’s threat perception. In a study on the relationship between insight and social support variables, patients were followed between 2 ½ and 3 ½ years after discharge from index hospitalization (McEvoy, Freter, Everett, Geller, Appelbaum, & Apperson et al.,

1989). Patients with greater insight were significantly less likely to be readmitted to the hospital over the course of follow-up. There was also a trend for patients with greater insight to be more adherent to treatment 30 days after discharge.

Intervention research guided by theoretical models has also investigated individual variables that influence individuals’ decision-making; such variables include attitudes toward mental illness/medications, recovery style, and insight (Kemp, et al.,

1996, 1998; Trait, Birchwood, & Trowler, 2003). Trait et al. (2003) found no relationship between insight and treatment engagement, but reported that recovery style was independent of insight and more predictive than insight in treatment engagement. In an investigation of compliance therapy on psychiatric medication adherence, Kemp et al.

(1996) gave participants several attitudes toward medication inventories, a measure of insight, and a self-report scale predictive of compliance. Study participants who received the intervention showed improved insight, attitudes, and compliance, as compared to a control group. Attitude toward psychotropic medication was also a significant predictor for compliance at a six-month follow-up. Negative attitudes toward medication and/or therapeutic treatment, especially medication and/or treatment efficacy, were associated with lower perceived benefits of adherence to treatment regimens.

Lack of insight is often considered synonymous with either a lack of awareness of mental illness and/or attribution of symptoms to other causes. Lack of insight has been linked to poor treatment outcome, especially for individuals with psychosis (Amadour et

27 al., 1991). Lysaker, Bell, Milstein, Bryson, and Beam-Goulet (1994) found that lesser insight into psychiatric illness was associated with nonadherence to a psychosocial work program. Specifically, poor insight was found to be positively associated with fewer weeks of participation, poorer social skills, and poorer personal presentation in the fifth week of work.

In an article on interventions to improve medication adherence in schizophrenia,

Zygmut et al. (2002) reviewed 39 studies. They reported that only one third of these studies found significant treatment effects. Studies employing clinical interventions focused on concrete problem solving, and motivational interviewing was more efficacious than psycho-educational interventions. Studies that combined different treatment modalities were also more effective than studies focusing solely on educational goals. These investigators also found studies directly targeting medication adherence to be more efficacious than studies of broader treatment interventions.

Individual dynamic characteristics, motivation, and TTM. In a study utilizing motivational interviewing for a population of dually diagnosed outpatients, the investigators found that readiness to change increased, as did short-term treatment adherence (Carey et al., 2002). A second study (Swanson, Pantalon, & Cohen, 1999) reported that initial outpatient adherence was significantly associated with having participated in a short-term, inpatient, motivation interviewing therapy session prior to discharge from an acute hospitalization. Studies of motivational readiness (Blanchard,

Morgenstern, Morgan, Labouvie, & Bux, 2003; Pantalon & Swanson, 2003) have obtained mixed results. Blanchard et al. (2003) investigated readiness for change, a central component of TTM, using the University of Rhode Island Change Assessment

Questionnaire (URICA) (McConnaughty, Prochaska, & Velicer, 1983). Motivation was assessed by considering responses to questions on intrinsic motivation, and legal coercion

(external motivation) on a measure of alcohol-related behavior. Neither the readiness score nor the motivation variables predicted completion of a 12-session outpatient program for individuals seeking substance abuse treatment. Pantalon and Swanson

(2003) found that for a population of dually diagnosed individuals, participants with lower readiness scores (as measured by the URICA) demonstrated greater treatment adherence in an inpatient and aftercare outpatient setting, as compared to individuals with higher readiness to change.

Ryan, Plant, and O’Malley (1995) have noted that despite the conceptual importance of motivation to the outcome of behavior change, the empirical evidence for its value has been mixed. Based on the theory of motivation and self-determination (Deci

& Ryan, 1985, 1987), Ryan et al. (1995) noted that strength of motivation is only one component, and that the reasons for pursuing or not pursuing change are equally important. Ryan et al. developed a treatment motivation questionnaire (TMQ) that measures the perceived locus of causality, or motivation. In their study of treatment adherence in an 8-week program for individuals with alcohol use disorders, they found participants with both high internal and external motivation to be most adherent, followed by individuals with high intrinsic motivation. Individuals with only high scores on external motivation were found to be the least treatment adherent. These results underscore the multifaceted aspects of motivation, and might help to explain the counterintuitive results from the URICA. For example, an individual might indicate a readiness to change but report this for a variety of reasons, such as feeling coerced or

29 wanting to enter treatment but not take medicine. The utility of the construct of motivation for the prediction of treatment adherence might be improved if the URICA were utilized in conjunction with the TMQ, as will be done in the current study.

Motivational interviewing (Miller & Rollnick, 2002), which is theoretically consistent with TTM, has been used increasingly and appears promising in terms of efficacy (Burke, Arkowitz, & Menchola, 2003). A recent meta-analysis (Burke et al.,

2003) found that slightly modified motivational approaches, also known as adapted motivational interviews (AMIs), are comparable in effectiveness to other treatments as reflected by moderate treatment effects (from .25 to .57). However, most of these studies have not investigated why the AMIs are efficacious, so AMI’s mechanisms of action need to be further researched.

In a review article focused solely on randomized controlled trials to enhance medication adherence, McDonald, Garg, and Haynes (2002) included 10 studies that involved psychiatric populations. In addition to random assignment to conditions, these studies also included measures for both mediation adherence and patient treatment outcome (such as relapse, hospital free days, social functioning, etc.), at least 80% of the sample had to be included in the follow-up, and for studies with initial positive findings the follow-up time period had to be at least six months. The focus of the studies was on schizophrenia, acute psychosis, or depression. McDonald et al. concluded that combination interventions and compliance therapy (Kemp et al., 1996, 1998) improved both adherence to medication and additional clinical indicators. Family therapy was also found to be effective, although the results were more mixed. The interventions targeted at patient education were generally unsuccessful.

Social Factors

In their review of the aftercare literature, Klinkenberg and Calsyn (1996) found that marital status either increased adherence or was unrelated to it. However, they also pointed out that many people with serious mental illnesses reported their status as single, so there may be insufficient variation or range in this variable in research samples to detect a potential significant difference. Olfson et al. (2000) found that a significant predictor for poor medication compliance after hospital discharge for individuals with schizophrenia was the family’s refusal to become involved in the patient’s treatment while in the hospital. Delaney (1998) investigated whether clubhouse attendance was associated with improved medication adherence and reduced hospital recidivism for individuals with longstanding, severe mental illness. She compared individuals across three different types of medication modalities-- intramuscular, oral, combination of intramuscular and oral-- and across two conditions-- medication management only, and medication management with clubhouse attendance. Her findings showed that clubhouse attendance significantly reduced rehospitalization as compared to the medication management group only. She also found that this effect was most pronounced for individuals in the oral psychotropic medication group. She speculated (but did not empirically test) that this effect might have been due to the supportive and normalizing atmosphere of the clubhouse, where members might bring their medications, talk about them, and compare them.

Compton et al. (2006) reported that Axis IV problems related to the primary support group (for example, death of family member, disruption of family, health problems in the family, etc.) significantly and independently increased the odds of initial

31 aftercare nonadherence following hospital discharge. Killaspy et al. (2000) found that her groups of newly referred outpatients and follow-up outpatients differed on the level of social support available to them. A total of 14% of follow-up patients listed no social support beyond mental health professionals, as compared to 2% in the newly referred patient group. A randomized, controlled clinical trial of psycho-educational single family therapy versus multiple family therapies found the latter group to be more effective than single-family treatment in extending remission, especially in patients at higher risk for relapse (McFarlane, Lukens, Link, Dushay, Deakins, Newmark et al., 1995). This may address the importance of network size (Perscosolido, 1991). Researchers have found that the patient’s perception of social support available for his recovery was significantly associated with improved treatment adherence and reduced rates of rehospitalization

(McEvoy et al., 1989; Young, Zonana, & Shepler, 1986).

A study on the effectiveness of consumer-provided case management services indicated that more positive attitudes toward medication compliance were related to older age, fewer symptoms, and a broader array of daily activities involving social relations, which in turn was related to better outcomes (Draine & Solomon, 1994). Other investigators (Dixon, Goldberg, Lehman, & McNary, 2001) looked at how psychiatric patients monitor, evaluate, and respond to their physical health functioning. Among the variables investigated was the size of their participants’ social networks. Smaller network size was one of only two variables associated with more severe symptoms.

Dixon et al. (2001) speculated that severe psychiatric symptoms might be responsible for a less vigorous social network, especially negative symptoms, which might make people withdraw from social contact (Hogarty, 1979). This latter finding might explain why

32 individuals with acute symptoms might also withdraw from contacts with their treatment providers, which was given as a reason for nonadherence in the Killaspy et al. (2000) study.

Illness/Clinical Determinants

Diagnosis, symptom severity, and other features of the illness. For variables such as length of stay and total number of hospitalizations, diagnosis was the only variable across studies found to have some predictive validity for aftercare treatment adherence following hospital discharge (Klinkenberg & Calsyn, 1996). These investigators reported that low aftercare adherence rates were found for patients with personality disorders, substance abuse disorders, and unclear diagnosis.

Symptom or illness severity might be a better predictor, as diagnostic reliably can vary greatly between geographic regions, or even hospitals in the same city, especially in managed care climates. For perceived distress or severity of symptoms, several researchers (Grunebaum et al., 1996; Centorrino et al., 2001) have found that more severe or acute symptoms are associated with higher appointment and/or medication adherence.

However, Killapsy et al. (2003) found that participants who missed their appointments indicated that they were more psychologically unwell, had lower social functioning, and were more likely to have a previous admission under the British Mental Health Act of

1983, compared with those who did attend.

Fenton, Blyler, and Heinssen (1997) cited five studies that linked illness severity at discharge from an inpatient hospitalization to poorer outpatient nonadherence. They also summarized findings from investigations into medication nonadherence and paranoid delusions, paranoid schizophrenia, and persecutory delusions. Fenton et al.

(1997) also reported that while findings appeared mixed with regard to the influence of these diagnostic characteristics on treatment adherence, a study by Van Putten,

Crumpton, and Yale (1976) indicated that individuals with paranoia and grandiosity were significantly more treatment non-adherent. Holzinger, Loeffler, Mueller, Priebe, and

Angermeyer (2002) also found grandiose delusions, attitudes toward medication, and quality of working alliance with the psychiatrist to be predictive of medication nonadherence.

The stigma of having a mental illness has also been investigated with respect to its relationship on medication adherence (Sirey et al., 2001). These investigators conceptualized stigma as a barrier to treatment, and found that adherence with an antidepressant regimen was associated with lower perceived stigma, higher self-ratings of illness, being older, and not having a co-occurring personality disorder.

Boyer et al. (2000) found that illness history distinguished between participants on the outcome of adherence following acute hospitalization discharge. Patients who kept their first outpatient appointment were more likely to have had a prior public inpatient psychiatric hospitalization relative to those who did not attend the first appointment.

Characteristics of the treatment regimen. One study found that individuals identified on the referral materials as taking psychotropic medications were more likely to attend their appointment than individuals who were not on medication (Kruse,

Rohland, & Wu , 2002). Another study, however (Centorrino et al., 2001), reported that adherence was lower for pharmacotherapy as compared to psychotherapy visits. These contradictory findings might be reconciled by considering the severity of the illness.

Kruse et al. (2002) studied first appointments to a clinic for individuals with serious mental illness, so being on mediation might have made her sample more acutely ill than

Centorrino et al.’s, which was recruited from a clinic providing longer-term outpatient care.

In several studies by Scott and colleagues (Adams & Scott, 2000; Scott, 2002;

Scott & Pope, 2002), adherence was predicted more accurately by individuals’ beliefs about themselves and their control over their disorder than by the side effects of medications. This is important, as side effects of medications have often been regarded as a very substantial reason why individuals stop taking medication. However, perceived potential benefits of medications might override the side effects. Perkins (2002) pointed out that the perceived benefit of medication has received insufficient research attention, and needs further investigation.

Apparently, only one study has considered the type of outpatient services provided (medication management versus psychotherapy) (Centorrino et al., 2001). The investigators reported in their abstract that individuals receiving outpatient psychotherapy were more adherent to their appointments than individuals receiving outpatient pharmacotherapy. However, adherence was also higher for visits that were routinely scheduled versus those scheduled on an emergency basis, and a correlation between schedule status and type of appointment was noted by the authors. Psychotherapy visits were routinely scheduled 85% of the time, whereas only 23% of medication visits were routinely scheduled. Further analysis by Centorrino et al. indicated that the schedule status was significantly associated with adherence, but not the type of visit. It appears that the authors misstated their findings in their abstract. To determine if different outpatient

35 services have different adherence rates when, for example, the type of scheduling is held constant, requires further research.

Satisfaction with the treatment regimen or consumers perspectives of the effectiveness of services have not been frequently studied as variables affecting treatment adherence. However, instruments for services evaluation by consumers such as the

CABS (Consumer Assessment of Behavioral Health Services Instrument) or MHSIP

(Mental Health Statistics Improvement Consumer Survey—Adult Survey Version 1.1) have been developed. More specifically the MHSIP proves an outcomes subscale that examines the effect of services receipt on consumers housing, relationships with family members, employment and so forth.

Institutional and Cultural Determinants

Another line of research investigating how individuals use mental health services has considered informal coercion (such as pressure from family, friends, social services, or the police, to enter the hospital), or formal coercion (Pescosolido, 1998). However, while some consumer advocates are strongly opposed to all types of forced treatment

(Campbell & Schraiber, 1989), empirical research on treatment coercion has yielded mixed results. Rain, Williams, Robbins, Monahan, Steadman, and Vesselinov (2003) found that perceived coercion at index hospitalization was not related to either appointment or medication adherence over a 1-year follow-up period. By contrast, a study conducted in Finland (Kaltiala-Heino, Laippala, & Salokangas, 1997) found that participants who felt that their admission was coerced were less likely to take medication,

use mental health center services, or show improvement in functioning or symptoms over

time.

Currently, the research focus on civil commitment has shifted to outpatient commitment and its relationship to treatment adherence. Only two clinical trials have been conducted to date, one in New York City (Steadman, Gounis, Dennis, Hopper,

Roche, & Swartz et al., 2001) and the other in North Carolina (Swartz, Swanson,

Wagner, Burns, Hiday, & Borum, 1999). These studies yielded inconsistent results. The intensity of aftercare treatment and degree of enforcement in outpatient commitment differed between the two studies. The investigators in New York City considered outcomes such as rates of rehospitalization, arrests, quality of life, psychiatric symptoms, and homelessness. The investigators noted several limitations, including small sample size, and ultimately did not find significant differences between the group with mental illness under outpatient commitment and a control group (also with mental illness) receiving intensive services without court commitment. The North Carolina study found that those under intensive outpatient commitment (i.e., 180 days or more) had higher rates of involvement in mental health treatment, including receiving prescribed medications. They also reported that commitment reduced negative outcomes such as illness relapse, violent behavior, victimization, and arrest. The authors cautioned that these results applied only to participants who also received intensive mental health services; it was therefore not possible to separate the effects of the commitment and service engagement.

Outpatient commitment has also been described as associated with increased mediation adherence (Hiday & Scheid-Cook, 1987) and reduced rehospitalization rates

(Fernandez & Nygard, 1990; Munetz, Grande, Kleist, & Peterson, 1996), although results

37 of these studies have been criticized on several grounds (mainly methodological) by the

Bazelon Center for Mental Health Law (Bazelon CMHL, 2001).

Cultural competence was investigated in a study in Australia. Ziguras et al. (2001) found better treatment adherence when clients had a case manager from the same ethno- linguistic background.

Provider Determinants

Clinical setting. While there is no single set of socio-demographic variables that characterize a treatment non-adherent individual, system level variables have been consistently reported to be predictive of treatment nonadherence. A system’s responsiveness, availability, and convenience of treatment provision are important in the

HBM category of perceived barriers. A comprehensive review of studies involving the

HBM (Janz & Becker, 1984) has noted that one of the strongest components of the model across a wide variety of studies was perceived barriers.

In mental health treatment adherence research, these variables have rarely been measured from the consumer perspective (but see Pollack et al. 1998 for a qualitative approach). Instead, proxy variables, such as actual waiting time for an appointment, and travel distance to the appointment have been used. One of the most consistent influences that increased nonadherence was a longer waiting period from referral to appointment

(Chen, 1991; Grunebaum et al., 1996; Nicholson, 1994). This has also been found to predict aftercare appointment attendance following discharge from an inpatient hospital stay. When the wait between hospital discharge and the initial aftercare appointment is longer, aftercare attendance drops significantly (Axelrod & Wetzler, 1989; Carpenter et al., 1981; Compton et al., 2006). Compton et al. (2006) recently found that not having an

38 established outpatient clinician independently and significantly increased the odds of nonadherence to a scheduled first aftercare appointment following hospital discharge.

While the study sample included individuals with no outpatient services history (possibly attenuating their finding), this finding, nevertheless, has important policy and programmatic implications. Having an established clinician provides continuity of services to the consumer, and might improve communication and continuity of care between inpatient and hospital providers.

Linkage strategies have been investigated by several researchers and consistently found to significantly increase rates of treatment adherence (Appleby et al. 2001; Boyer et al., 2000; Olfson et al., 1998; Stickney et al., 1980). For example, Stickney et al.

(1980) raised the rates of aftercare adherence from 22% to 75% when a nurse met with the patient prior to discharge, and an appointment was scheduled with the same nurse.

This study had several methodological problems (e.g., consecutive recruitment into the different referral methods) and therefore, potentially confounded other system changes that occurred over the length of the study with the different referral methods.

Nevertheless, the data may still suggest that aftercare adherence can vary as a function of different linkage strategies.

Boyer et al. (2000) investigated patient risk factors for not accepting or completing outpatient referrals and the effectiveness of several linkage strategies between inpatient and outpatient follow-up care. Their retrospective study consisted of 229 inpatients who were given referrals to follow-up outpatient care upon discharge from an acute inpatient psychiatric hospitalization. The investigators found that when communication between inpatient and outpatient providers occurred before the patient’s

39 discharge, the adherence rate was 43%, compared with 19% in patients with whom communication did not occur.

The relationship between the health care provider and patient. A good therapeutic alliance between the mental heath care professional and the patient has consistently been found to increase treatment compliance with psychiatric medication (Holzinger, Loeffler,

Mueller, Priebe, & Angermeyer, 2002; Perkins, 2002; Weiss, Smith, Hull, Piper, &

Huppert, 2002). Such an alliance was also found to be important in retention in outpatient care for individuals with serious mental illnesses, and is furthermore thought to be associated with increased satisfaction with care, and reductions in violent behavior

(Eisen, Dickey, & Sederer, 2001). In their study of predicting medication noncompliance after hospital discharge, Olfson et al. (2000) found that patients who became medication noncompliant after discharge were significantly less likely to have developed a good therapeutic alliance during hospitalization, as measured by inpatient staff reports. Beck

(2001) proposed a cognitive therapeutic model to deal with medication adherence, which she suggested is strongly associated with a good therapeutic alliance between practitioner and consumer. Recently, an entire volume has been edited on the topic of treatment compliance and the therapeutic alliance for individuals with serious mental illness, substance use disorders, and developmental disabilities from a variety of perspectives

(Blackwell, 1997).

The Measurement of Treatment Adherence

In practice, treatment adherence is not a dichotomous outcome (Turk &

Meichenbaum, 1987). One of the first problems encountered in the study of adherence to treatment for individuals with chronic medical illness was how to define nonadherence.

Epstein and Cluss (1982) noted that it was unclear what distinguished acceptable from unacceptable treatment adherence for most medical conditions. For example, in many illnesses the question of adequate rate of medication adherence remains unanswered.

While 100% compliance might be optimal, perhaps an adherence rate of 85% would be sufficient to yield significant symptom reduction or illness remission. Measuring adherence is particularly problematic when studying medication use (Meichenbaum &

Turk, 1987).

For the purpose of the current study, aftercare adherence will be measured as a dichotomous outcome. Many individuals released from inpatient hospitalization do not follow-up with recommended aftercare (Boyer et al., 2000; Nelson et al., 2000); such aftercare usually consists of a follow-up appointment with a community mental health care professional, either for psychopharmacological treatment or some combination of medication, therapy, and case management services (Solomon, Gordon, & Davis, 1986).

For continuity of care to be successful, and for outpatient providers to engage and retain the consumer in often long-term treatment regimens, the consumer must first attend the initial follow-up appointment. Attending the initial appointment may be determined by different factors, as compared to who may remain in treatment once the initial contact has been made. The current study investigated whether the HBM and/or motivational constructs, including the TTM, provided a theoretical framework for the prediction of attendance of the initial aftercare appointment following discharge from acute inpatient hospitalization. The identification of theoretically-based predictors for initial aftercare adherence is thought to have important implications for inpatient treatment providers’ communications and therapeutic interventions during the patient’s hospitalization.

Aim of the Current Study

The current research investigated predictors of adherence with aftercare appointments following an inpatient psychiatric hospitalization, and hospital recidivism within three months following the index discharge. Three predictor domains were examined:

(1) clinical characteristics (discharge diagnosis, number of prior hospitalizations,

substance use prior to hospitalization, past aftercare non-compliance, involuntary

or voluntary admission to index hospitalization, length of hospitalization,

discharge under outpatient commitment, and type of housing at discharge--alone,

family, friends, shelter, recovery house; socio-demographic characteristics

(gender, age, race, marital status); system level variables (case management

services, time interval between patient’s discharge and first scheduled

appointment, and outpatient referral sites -- walk-in clinic, community mental

health center);

(2) health beliefs modeled on the HBM: (a) perceived susceptibility: insight into

illness; (b) perceived benefits: drug attitudes; (c) perceived barriers: past access to

outpatient services, and past outpatient consumer-provider relationship

experiences; (d) self-efficacy; and,

(3) motivation modeled on the TTM: individuals’ readiness to change, and

motivation for treatment.

The current study used the HBM and TTM as frameworks to study aftercare adherence in a population of discharged psychiatric inpatients. These models conceptualize decision making as “rational,” and place the locus of decision making

42 within the individual. Analysis examined the ability of the HBM and TTM models to predict individuals’ initial aftercare appointment adherence and hospital recidivism over a

3-month follow-up period. Sequential logistic regression analyses were conducted to test if the HBM, motivational models like the TMM, or a combination of the HBM and motivational construct provide a better explanatory framework for each outcome variable, and to obtain the likelihood that a person with certain beliefs, attitudes, and motivations kept the initial aftercare appointment, and/or was rehospitalized. The study recruited individuals with a history of treatment nonadherence, who have been underrepresented in adherence outcome research (Zygmut et al., 2002). Several investigators have observed that interventions on inpatient units might be well suited to reach individuals with a high risk of aftercare nonadherence and subsequent symptom exacerbation and rehospitalization. Results from the current investigation were conceptualized to aid the development of interventions that are built collaboratively with consumers, incorporating their beliefs, attitudes, and experiences.

CHAPTER 2: METHOD

Participants

Potential participants were recruited from the Psychiatric Medical Care Unit

(PMCU) at Hahnemann University Hospital. Data collection took place from August 2,

2004 to May 31, 2005. Patients were prescreened according to the following eligibility

criteria: (a) eligible participants were between the ages of 18-60, fluent in English, (b) had a minimum inpatient length of stay of 48 hours or more, (c) were Philadelphia residents, (d) were discharged to the community (private home, friends, family, shelter, and so on), (e) had follow-up appointments made by the hospital staff or themselves, as noted in the participant’s chart (this included referrals to a walk-in clinic), (f) had no health insurance or Medicaid Managed Care Insurance (managed by Community

Behavioral Health), and (g) had at least one previous instance of any outpatient treatment

(at least one session, intake, and visit) within the last two years, as documented in the patient’s chart, or per patient self-report. Potential participants also met DSM-IV (APA,

1994) criteria for serious mental illness, defined as a schizophrenia spectrum disorder,

major depression spectrum disorder (except disorders in partial or full remission),

depression NOS, psychosis NOS, or a major affective disorder (except disorders in partial or full remission), bipolar disorder NOS, bipolar II disorder, and mood disorder NOS.

Because epidemiological data have indicated high rates of co-occurring substance abuse in individuals with SMI, and because the literature indicates that these individuals have poorer adherence rates than individuals with either of these two disorders, participants with co-morbid diagnoses were included in the study. Subjects were entered in the study if they provided written, informed consent.

Patients were not eligible for study participation if they had a documented diagnosis of mental retardation, pervasive developmental disorder, or a psychiatric disorder due to a medical condition, and/or a serious, likely terminal medical condition that required the individual to receive ongoing medical treatment (such as end-stage renal disease or terminal cancer). They were also not eligible if they were discharged or transferred to another psychiatric hospital; to an extended acute facility, nursing home, and residential placement (CRR), jail/prison, or inpatient drug and alcohol rehabilitation center; left the hospital against medical advice or AMA; had private health insurance or primary

Medicare hospital insurance (Part A); had a maximum length of stay exceeding 45 days; and remained in the hospital for more than 8 days following their research interview.

Potentially eligible patients were approached as closely as possible to their discharge date.

A total of 561 patients were recorded as having been hospitalized during the data collection period, including individuals rehospitalized during this time. All recorded admissions were prescreened for study eligibility. A total of 354 prescreened patients, or

63% of all admissions, were ineligible for study participation as established by the eligibility criteria. The PMCU is located in a large, urban, acute care hospital, and is one of the few medical psychiatric units located in the city of Philadelphia. As such, this unit has a larger percentage of older patients, terminally ill patients, and individuals with co- occurring medical conditions. Patients were excluded from potential study participation for the following reasons: under 18 or over 60 years of age (9%); staying on the ward for less than 48 hours (17%); not fluent in English (2%); not residing in Philadelphia (6 %); having private health insurance or Medicare as primary insurance (15%); discharge to

45 another inpatient hospital/inpatient substance abuse program, nursing home, jail, extended acute facility (5%); staying on the ward for longer than 45 days (2%); having mental retardation, a pervasive developmental disorder, or psychiatric symptoms secondary to a medical illness or head injury (7%); end stage-terminal illness (1%); no documented or self-reported instance of any outpatient treatment within the last 2 years

(12%); leaving against medical advice (3%); having already been interviewed ( 6%); meeting more than one exclusion criteria (12%); and other (1%) (e.g., being in the hospital with a “fake identity”).

A total of 197 patients, or 35% of all admissions, met inclusion criteria. Of the study eligible group, 48% (N=94) of potential participants were discharged without having been approached by a research associate. Of all eligible patients, 52% (N=103) were approached for possible study participation. Of this group, 18% (N=19) refused study participation, and 82% (N=84) agreed to participate. The study sample therefore constituted 43% of all eligible patients (see Appendix B for the recruitment flow sheet).

Procedure Research associates identified themselves and obtained permission to ask the patients one screening question to ascertain if they had attended outpatient services in the past 2 years. Patients who answered “no” were thanked for their time and informed that they were not eligible to participate in the study. Patients who indicated “yes” (potential participants) were asked of their interest in participating in a research interview regarding their views on psychiatric medications, the reasons for their hospitalization, the nature of their mental illness, and their past outpatient treatment experiences. Patients were also told they were going to be paid $5 for their time. Informed consent was obtained from patients willing to proceed. Each participant had the consent form read to him to avoid

46 excluding those who could not read. Interviewers were trained to proficiency on the administration of the Awareness of Illness Interview (AII), and on how to conduct structured research interviews. Participant responses were recorded as close to verbatim as possible by the interviewer. All other self-report measures were read to the study participants. All participants were paid $5 for participating in the interview.

Consented participants’ medical charts were reviewed using a medical record abstraction form developed for this study. This form abstracted three general categories of participant data: (1) clinical characteristics (discharge diagnosis, number of prior hospitalizations, substance use prior to hospitalization, past aftercare non-compliance, case management services, involuntary or voluntary admission to index hospitalization, length of hospitalization, discharge under outpatient commitment); (2) socio- demographic characteristics (gender, age, race, marital status, type of housing at discharge); and (3) system level variables (time interval between patient’s discharge and first scheduled appointment, and type of outpatient referral site). If such information was not recorded in the patients’ charts, interviewers were instructed to obtain participants’ self-report with regard to the following variables: best estimate of the number of prior inpatient psychiatric hospitalization, having a case manager, type of housing at discharge, and name of referral site.

Participants were excluded from the study following consent, commencement and/or completion of the research interview for the following reasons: (1) starting the interview but not finishing it due to discharge during the interview; (2) deciding to sign themselves out against medical advice (AMA) prior to their discharge date; (3) discharge planned to the community, but discharge plans were changed to another inpatient setting

47 after consent and interview; or (4) participant was not discharged when indicated to research associate and remained in the hospital for more than 8 days following the interview. Excluded participants’ interviews were not used in any of the analyses.

After the study was thoroughly described to the potential participants, written

informed consent was obtained from the 84 patients. Eighty-three consented participants

complete the research interview. One participant was discharged during the interview

and chose not to complete it. Subsequent to the interview, 9 additional participants were

excluded from the final sample for the following reasons: (1) they did not meet the

eligibility criteria (1 person had already been inpatient hospitalized more than 45 days at the time of the research interview; 1 person had a terminal illness); (2) they were discharged to an inpatient facility (N=2); (3) they had been mistakenly interviewed a second time during a rehospitalization (N=1); (4) they remained at the PMCU for more than 8 days following the interview (N=2); (5) study participants did not exist in the

Community Behavioral Health database, making it likely that these individuals were either not Philadelphia residents, or had gained hospital admission under a false identity

(N=2). The final study sample consisted of 74 study participants.

There were no significant differences between the 74 participants and the 19 who

declined to participate on the variables of age, gender, length of stay, or insurance status.

Additionally, no significant differences in age, gender, length of stay, or insurance status were found when comparing the study sample with the group that was eligible, but not approached. No other variables were available for comparison, as no informed consent had been obtained from patients to collect this information.

Measures

Socio-demographic information was abstracted from each participant’s medical chart. Clinical information such as discharge diagnosis, number of prior hospitalizations, past aftercare nonadherence, case management services, legal admission status, and outpatient commitment status were also recorded verbatim from the study participant’s chart. Participants were considered to have a substance abuse problem if the history or records of toxicology screens indicated current use of alcohol and/or illegal substances such as sedatives (excluding sedatives if prescribed and taken as prescribed), cocaine, cannabis, stimulants, opioids, hallucinogens, or inhalants. System level variables, such as type of outpatient referral site and length of time from discharge date for first outpatient appointment, were also abstracted from the charts.

Drug Attitude Inventory – Short Form (DAI-10) (Hogan, Award, & Easterwood,

1983). The original version of the scale (DAI-30 Long Form) consisted of 30 items covering seven categories: subjective positive, subjective negative, health and illness, physician control, prevention, and harm (this is 6 unless health and illness are separate).

A shorter version consisting of 10 key items was subsequently developed (the DAI-10).

The DAI-10 is a widely used ten-item self-report inventory that was developed to assess how the attitudes of individuals with schizophrenia toward medication may affect compliance. This instrument has since been used with other psychiatric populations. In the current study, the DAI-10 was utilized to measure participants’ attitudes toward taking medication for mental health problems. The items are self-report statements that the participant rates as true or false. The DAI-10 is concise, easy to administer, and has well-established psychometric properties. It has high face validity, as items on DAI-10

49 were selected specifically based on comments made by patients regarding their treatment.

The scale has been shown to have acceptable test-retest reliability, high internal consistency, and discriminant, predictive, and concurrent validity (Hogan, Awad, &

Eastwood, 1983; Hogan & Awad, 1992).

Awareness of Illness Interview 1(AII) (Cuffel, Alford, Fischer, & Owen, 1996).

The AII is a 7-item semi-structured interview used to assess the participants’ awareness

of mental illness and perceived need for psychiatric treatment. Interviewers rate

participants’ responses on a 5-point scale from 1 (clear awareness) to 5 (no awareness).

The AII has good internal consistency (alpha coefficient = .84) and good test-retest

reliability (ICC = .79) (Cuffle et al., 1996).

Mental Health Confidence Scale2 (MHCS) (Carpinello, Knight, Markowitz, &

Pease, 2000). The MHCS is a 16-item self-report measure of self-efficacy among

individuals with mental disorders, grounded in the recovery of the mental illness model,

which measures optimism, coping, and advocacy. Individuals completing the scale are

asked to rate their confidence on a scale ranging from 1 (very non-confident) to 6 (very

confident). Reliability estimates as measured by coefficient alpha were .94 for the full

scale, and .91, .90, and .80 for the optimism, coping, and advocacy sub-scales,

respectively (Carpinello, Knight, Markowitz, & Pease, 2000).

1 AII questions 1-6 were utilized to obtain the full scale AII. AII question 7 was not included in the semi- structured interview, and instead, participants were asked to rate themselves on a scale from 1-10. AII-7 was utilized as a separate variable in the analysis on aftercare adherence.

2 MHCS question 5 “How confident are you now that you can boost your self-esteem” was accidentally not included in the questionnaire booklet. The full scale MHCS is therefore comprised of 15 items and not 16, as in the original study and the Optimism subscale is comprised of 5 scale items instead of 6.

Treatment Motivation Questionnaire 3(TMQ) (Ryan, Plant, & O’Malley, 1995).

This measure is based on the role of self-determination and internalization for motivation for psychotherapy (Deci & Ryan, 1985). The TMQ is a self-administered 26-item instrument developed to assess motivation of individuals with substance use disorders for entering and remaining in treatment. This measure was used to gauge motivation toward treatment and the role of intrinsic and external motivation in this process. Factor analysis of the TMQ yielded an 11-item internal motivation factor, a 6-item interpersonal-help- seeking factor, a 4-item external motivation factor, and a 5-item confidence-in-treatment factor. The factors were internally consistent, with coefficient alphas ranging from .70 to

.98. The TMQ factor scores were intercorrelated, and correlated with clinician ratings at intake. All correlations were significant except one, and the patterns of these relationships indicated good initial construct validity of the TMQ. The associations between TMQ and a variety of psychological or associated measures of addiction revealed that the TMQ has moderate concurrent validity. Predictive validity was demonstrated by showing that (a) higher initial internal motivation for treatment was positively related to outcome at eight weeks, (b) individuals who scored high on both initial internal and external motivation were even more likely to persist in treatment, and

(c) individuals who were high on only external motivation were most likely to drop out of treatment. This instrument has also been used to assess how motivation is altered during the course of treatment (Cahill, Adinoff, Hosig, Muller, & Pulliam, 2003). The current

3 The original TMQ has Likert scale numeral anchors ranging from 1-7, with anchor descriptors ranging from “not at all true,” to “somewhat true,” to ”very true.” The current study erroneously used a Likert scale with numerals ranging from 1-5; however the same anchor descriptors were used as in the original TMQ.

51 study used the subscales of internal and external motivation. The TMQ does not allow for calculation of a full scale score.

University of Rhode Island Stages of Change Assessment 4(URICA)

(McConnaughy, Prochaska, & Velicer, 1983). The URICA is a 32-item self-report

measure of attitudes toward changing problem behaviors. It has mainly been utilized in

studies investigating addictive behaviors, such as smoking, drugs, and alcohol use. It

includes four subscales, which measure 4 of the 5 stages of change: precontemplation,

contemplation, action and maintenance. Each of those sub-scales contains 8 items.

Responses are given on a 5-point Likert scale ranging from 1 (strong agreement) to 5

(strong disagreement). The subscales can be combined arithmetically (C + A + M – PC)

to yield a continuous score that can be used to assess readiness to change at entrance to

treatment (Project MATCH Research Group, 1997). The URICA can be used to assess

clinical process and motivational readiness for change, as well as to measure process and

outcome variables for a variety of health and addictive behaviors. The current study used

this measure to assess participants’ readiness to change. The URICA has been found to

be reliable and to identify four distinct motivational profiles (Carney & Kivlahan, 1995;

DiClemente & Hughes, 1990). The predictive and construct validity of the URICA has

been established for use with individuals with alcohol disorders (Carbonari &

DiClemente, 2000; Willoughby & Edens, 1996), but the predictive validity of the

readiness score has been mixed (Project MATCH Research Group, 1997). Concurrent

validity of the readiness score has been shown to be good, but the tool performed poorly

in predicting final treatment outcomes (Blanchard et al., 2003). The only study to date

4 The URICA was not administered to two study participants due to interviewer error.

52 with a dually diagnosed population (Pantalon & Swanson, 2003) replicated the 4-factor structure, and showed that the 4 subscales had acceptable internal consistency, with alphas of .76 and higher.

Consumer Assessment of Behavioral Health Services Instrument (CABHS)

(Eisen, Shaul, Clarridge, Nelson, Spink, & Cleary, 1999). The CABHS was designed to assess the quality of mental health and substance abuse services and evaluate insurance plans that provide such services. This survey measures the following domains: (a) access to care, including timeliness of appointments and availability of telephone services, (b)

quality of personal interactions and communication with providers, (c) information given

by providers to consumers and by consumers to providers, (d) continuity and coordination of care, and (e) global evaluation of healthcare plan enrollment and payment for services.

It also provides information about the quality of the health plan, including access to information and services provided by the plan, administrative burden, and global

evaluation of the plan. Eisen, Shaul, Leff, Stringfellow, Clarridge, and Cleary (2001)

found that a 5-factor solution accounted for 60% of the total variance in the CABHS.

The five factors were: (1) getting care quickly, (2) consumer-provider relationship, (3)

information given by clinicians, (4) plan access and administrative burden, and (5)

waiting more than 15 minutes past appointment time. Internal consistency reliability for

four of the five domains was acceptable (Cronbach’s alpha ≥ .70). The current study

used the consumer-provider relationship subscale which had good internal consistency

(alpha coefficient = .87).

The Mental Health Statistics Improvement Program Consumer Survey--Adult

Survey Version 1.1 (MHSIP-CS) (February, 2000). The MHSIP-CS was developed by a group that included consumers, family members, researchers, and federal, state and local mental health agency representatives. In 1976, the National Institute of Mental Health initiated the Mental Health Statistics Improvement Program (MHSIP). The MHSIP is supported by the Center for Mental Health Services (CMHS) and the Substance Abuse and Mental Health Services Administration (SAMHSA). The current official version of the MHSIP-CS has 28 items5 (follows an original 40-item and 21-item version). It

addresses consumer perceptions of three main domains: (a) access to care, (b)

quality/appropriateness, and (c) outcomes. Scores are developed for each domain by

calculating average percentage scores for those who score "strongly agree/agree, are

neutral, or strongly disagree/disagree." Respondents use the same 5-point rating scale for

each statement. A study by Eisen, Shaul, Leff, Stringfellow, Clarridge, and Cleary (2001)

found that reliability for each of the three domains was high, α= .81 for access to care,

α= .89 for quality/appropriateness, and α= .91 for outcome of treatment. Corrected inter-

item correlations for items within the MHSIP subscales ranged from .39 to .73. The

reliability coefficients for a previously tested 21-item version ranged from .65 to .87 for

the different scales (Ganju, Wackwitz, & Trabin, 1998). The MHSIP survey has been

widely implemented in several states (Teague, Ganju, & Hornick, 1997), and found to be reliable. In the current study, only the access to treatment/care subscale was used. The

questions that have shown high factor loading on this construct assess the convenience of

5 No published studies could be located that evaluated the current 28-item version for its reliability and validity. The access to care subscale in the 28-item version differs from the 40-item version evaluated by Eisen et al. only in the deletion of one item.

54 the location of the services, and further deal with the availability and access to services, treatment staff, and psychiatrists (Eisen, Shaul, Leff, Stringfellow, Clarridge, & Cleary,

2001).

Outcome Measures

Outcomes were obtained in the form of authorization data (also called secondary data) for behavioral health services (all authorized mental health and substance abuse services) that were utilized for each member within their respective 3-month follow-up time period in the final study sample. These data were obtained from Community

Behavioral Health (CBH) following an additional 3-month reporting grace period, following the end of the 3-month follow-up period of the last study participant discharged from their index hospitalization. In other words, secondary data for the last study participant discharged from the hospital were obtained 6 months after their actual index hospital discharge date. An important safeguard to the accuracy of the secondary data is the requirement for providers to update authorization data on a regular basis. This means that while the present data represents CBH authorization data, the difference between having authorization data and claims data for inpatient services at the time when the data was requested is very small. Additionally, outpatient services require no pre- authorization from CBH, meaning that all secondary data for outpatients’ services presented actual contacts between the study participant and the provider.

The authorization data provided by CBH specified codes for psychiatric inpatient and other behavioral health services. The secondary data on inpatient hospitalizations were used as provided. It was coded as a binary variable for each study participant (yes =

55 participant was psychiatrically rehospitalized during the 3-month study period, no = study participant was not rehospitalized).

The remaining services data were further differentiated by CBH into the following categories:(1) Community Support Psychiatric Services (Crisis Residence,

Intensive Case Management Services); (2) Outpatient Psychiatric Services

(Biopsychosocial Evaluation, Consultation Fees initials and follow-up, RN/LPN home visit); (3) Other Psychiatric Services (Residential Services-Other);(4) None Hospital

D&A Services (Long Term Rehab, Short Term Rehab); (5) Partial Psychiatric

(Psychiatric Partial Program); (6) Partial D&A program (Intensive Outpatient Program).

(Consider uncapitalizing the categories above)

Outpatient services were conceptualized as a participant having at least one contact with an outpatient service provider. It was also coded as a binary variable for each study participant (yes = participant had an outpatient contact during the 3-month study period, no = study participant did have any outpatient service contact). Binary coding was necessary as the frequency of services received was not available for all of the service types in the secondary data. For the purpose of the present study, a participant was coded as having received outpatient services if he had received any of the above service codes with the exception of consultation fees, long- term rehabilitation and short- term rehabilitation services. Consultation fees were excluded, as this is a psychiatric consultation that a participant received while in a medical hospital, and this service is not necessarily participant initiated. Long-term rehabilitation and short-term rehabilitation services were excluded because they do not constitute outpatient service attendance.

Analytic Strategy and Statistical Methods

Power analysis. Cohen (1992) suggests .15 as a medium effect size for multiple and multiple partial correlations. No meta-analysis on appointment treatment adherence could be located. Conventionally, a medium effect size is selected if previous studies have not reported effect sizes, or no previous research on in a specific research domain exists. The program G*Power was used to calculate the number of participants necessary for a multiple regression analysis (Faul, & Erdfelder, 1992). Using 6 substantive predictors, α = .05, f2 =. 15, and power set at .80, a sample size of 98 participants would be required to evaluate a basic “risk” model. Using 15 substantive predictors (full model for aftercare; Table E6), α = .05, f2 =. 15, and power set at .80, a sample size of 139 participants would be required to evaluate a model that included all “risk” variables,

HBM variables, and motivational variables.

Post-hoc power analysis indicated that with 6 substantive predictors (risk factor models for both aftercare adherence and rehospitalization; Tables E6 & E8), α = .05, f2

=. 15, a sample size of 74 participants, the power of the analysis would be 0.65. For model 2 of aftercare adherence (Tables E6 & E7) for 12 substantive predictors, α = .05, f2 =. 15, a sample size of 74 participants, the power of the analysis would be 0.55. For model 3 of aftercare adherence (Tables E6 & E7) for 9 substantive predictors, α = .05, f2

=. 15, a sample size of 74 participants, the power of the analysis would be 0.55. Finally, for model 4 of aftercare adherence (Tables E6 & E7) for 15 substantive predictors, α =

.05, f2 =. 15, a sample size of 74 participants, the power of the analysis would be 0.41.

The power of the analyses to detect a significant effect if present is essentially the same

57 for models 2-4 (Tables E8 & E9) of hospital recidivism. These analyses contain 1 less variable in model 2 and model 4 respectively, so power for these analyses is 0.50 (model

2) and 0.44 (model 4) respectively. Such power levels are relatively low, which could mean that the study failed to detect a real-world effect.

Cohen power analyses were not calculated for the logistic regression models because the former rely heavily on R2 as a measure of variance accounted for, and there

are no satisfactory analogues for R2 in logistic regression models (Long, 1997).

However, it has also been reported that larger samples are required for logistic regression analysis because standard errors for maximum likelihood coefficients are large-sample estimates. Some authors have recommended about 50 cases per predictor variable

(Aldrich & Nelson, 1984; as quoted in Grimm & Yarnold, 1995, p.221).

Statistical methods. The primary goal of the present study was to observe predictors of adherence with aftercare appointments following an inpatient psychiatric hospitalization, and recidivism rates to inpatient hospitalization within three months

following the index discharge. Intraclass correlations (ICC) were used to determine

agreement between the original rating and secondary rating of the Awareness of Illness

interview. Both raters were blind to the outcomes, and the second rater was blind to the

ratings of the first rater. Disagreements by the raters were solved via discussion until a

mutually agreeable coding solution was reached.

Chi-square tests for categorical variables and T-tests for continuous variables

were used to identify patient risk factors for the first domain associated with failure to

keep initial outpatient appointments and to identify beliefs and experiences associated

with patients’ keeping appointments. The same analyses were conducted for the outcome

58 of being rehospitalized in the 3-month follow-up period. All tests used 2-tailed significance levels, and comparisons were considered statistically significant at the 5% level (alpha = .05).

Multivariate logistic regression analyses were conducted to: (1) assess the predictive validity of the HBM and TTM to provide an explanatory framework for aftercare attendance versus no aftercare attendance in a 3-month follow-up period.

Motivation was measured with the TMQ (internal and external motivation), readiness for change measured with the URICA, self-efficacy with the MHCS, insight into illness with the AII, attitudes toward medications with the DAI-10, access to past outpatient care with a subscale of the CABHS, and quality of past outpatient consumer-provider relationship with a subscale of the MHSIP-CS, (2) assess the predictive validity of the HBM and

TTM to provide an explanatory framework for hospital recidivism in a 3-month follow- up period. Motivation and aspects of the HBM were measured as described above, (3) investigate the likelihood that individuals’ positive attitudes toward psychiatric medications, better insight into their illness, higher levels of either intrinsic or intrinsic- extrinsic motivation, better previous consumer-provider relationship satisfaction, better previous access to outpatient treatment, and higher levels of self-efficacy can significantly predict their aftercare appointment adherence, and (4) investigate the likelihood that participants’ positive attitudes toward psychiatric medications, better insight into their illness, higher levels of either intrinsic or intrinsic-extrinsic motivation, better previous consumer-provider relationship satisfaction, better previous access to outpatient treatment, and higher levels of self-efficacy can significantly predict whether

59 the participant would return to an inpatient hospitalization during the 6–month follow-up time period.

CHAPTER 3: RESULTS

General Background Information

Of the 74 participants, 51% (N=38) reported that their main problem was their

mental health, 43% (N=32) indicated that their main problem was dual diagnosis, and 2%

(N=2) indicated that their main problem was neither a mental health nor substance abuse problem (see Table E1). Data on this question were missing for two participants. The sample was comprised of 55.4 % (N=41) male and 44.6% (N=33) female participants.

Participants were interviewed as close as possible to their discharge date. The mean days

and SD (mean ± SD) between interview and discharge were 1.6 ± 1.8, with a range of 0-8

days. Of 74 participants, 27% (N=20) were rehospitalized, 41.9% (N=31) had an

aftercare contact, and 17.6% (N= 13) had both an aftercare contact and a rehospitalization

in the 3-month follow-up period. Due to the nature of the secondary data, it was not

possible to establish in which order a subsequent outpatient and rehospitalization service

contact followed the index hospitalization discharge. Because an aftercare appointment

could either precede a subsequent hospitalization or follow it, a variable for aftercare

contact could not be used in the logistic regression models as a predictor of

rehospitalization.

Background Characteristics Rehospitalization of Participants

Of the 74 participants followed up, 27% (N=20) were re-hospitalized and 73%

(N=54) were not. Table E2 provides percentages and statistical tests of socio-

demographic, clinical and system variables by rehospitalization and no rehospitalization.

No significant differences were noted in self-reported problem, age, sex, race, marital

61 status, legal status at the index admission, past aftercare noncompliance, case management services, length of index hospitalization, discharge housing site, an

appointment made versus a walk-in clinic appointment, or substance use at time of

admission. A diagnosis of substance abuse or dependence has been shown to be

associated with more frequent readmission (Klinkenberg & Calsyn, 1996). However

these findings were not replicated in the current study.

Insurance. Rehospitalization varied significantly with respect to participant

insurance status χ2(1, N= 66) = 4.20, p = .040. Of 16 participants with county funding

(no insurance) at the time of index hospitalization, only one was readmitted in the 3-

month follow-up period, as compared to 32.0 % (N=16) with Medicaid insurance. While

all study participants met the Medicaid/no health insurance study eligibility criteria, the

actual type of insurance was not recorded for 10.8% (N=8) of the sample. Due to the

large amount of missing data for this variable, this variable was not used in further

analysis.

Number of previous psychiatric hospitalizations. Rehospitalized participants had a

larger mean number of prior hospitalizations (M=6.95, SD=7.24) as compared to the

participants not rehospitalized (M=3.45, SD=2.65). This effect was significant [t(71) = -

3.43, p = .003]. This indicated that participants with more prior hospitalizations were

more likely to be rehospitalized in the 3-month follow-up period.

Mental health diagnosis at discharge. There was also a significant difference in

the type of discharge mental health diagnosis between participants who were rehospitalized and those who were not. Patients who were diagnosed with schizophrenia spectrum disorders were rehospitalized significantly more often than participants with

62 bipolar and depression spectrum disorders (categories collapsed) during the 3-month period following the index hospitalization ( [N=10, or 41.7 %], versus depression/bipolar

disorder [N=10 patients, or 20 %]; χ2=3.86, df=1, p =.049).

Background Characteristics Aftercare Adherence

Of the 74 patients followed during the outcome period, 58.1 % (N=43) had no

aftercare contact and the remaining 41.9% (N=31) did (see Table E3). No significant

differences were noted in age, gender, race, insurance status, type of main self-reported

problem, legal status at the index admission, discharge mental health diagnosis, number

of previous psychiatric hospitalizations, discharge housing site, having an appointment

made versus a walk-in clinic referral, or substance use/abuse at time of admission. The latter finding was somewhat surprising given that having a substance use disorder has been linked to receipt of less aftercare (Klinkenberg & Calsyn, 1996). Participants’ history of previous aftercare noncompliance approached significance with respect to aftercare attendance in the 3-month follow-up period [χ2(1,N=74) = 3.56, p =.059].

Differences in participants’ marital status also approached significance on aftercare attendance [χ2(1,N=74)=3.74, p =.053]. A majority of the patients in both groups had

never married, although a higher number of single participants attended aftercare

appointments [93.5% (N=29)] as compared to participants who were

married/separated/divorced [6.5% (N=2)]. Although this variable had already been

recoded (categories collapsed), the low cell frequency makes any meaningful

interpretation of a significant result tenuous due to possible violation of sampling

adequacy (Garson, 2006).

Case management services. Having a case manager significantly differentiated between aftercare adherence and nonadherence [χ2(1,N=74)=7.64, p =.006]. Of 15 participants who had case management services, 35.5% (N=11) did have an aftercare outpatient contact as compared with 9.3% (N=4) who did not.

Length of index hospitalization. Participants who attended an aftercare appointment had an average index hospitalization of nine days (M=8.61, SD =6.53), compared with a shorter period of hospitalization for participants who did not (M=6.05, SD=5.59). This difference was significant [t(74) = -2.26, p = .027].

Health Beliefs, Rehospitalization, and Aftercare Adherence

DAI-10. As may be seen in Table E4, patients who were rehospitalized had a significantly less positive attitude toward psychiatric medication as compared to participants who were not rehospitalized [t(72)=2.34, p =.022]. There was no significant difference in mean Drug Attitude score between the participants who did and did not follow-up with aftercare (see Table E5).

AII (1-6; 7). There was no significant difference in mean Awareness of Illness scores (AII 1-6) between the participants who had aftercare and those who did not, or between the means of those rehospitalized and those who were not. Overall, participants evidenced clear to good awareness that they had a mental illness, and reported clear to good awareness that they needed treatment. Participants in both groups also rated themselves on how likely they would seek outpatient care after their discharge (AII-7).

There were no differences in group means for either outcome variable, and overall, participants rated themselves as highly likely to seek outpatient care.

MHSIP-Access to Care Subscale. There was no significant difference between aftercare/no aftercare groups, or between rehospitalization/no rehospitalization groups, on mean MHSIP access to care scores. Most participants’ responses were in the neutral to agreeable range regarding having access to care.

MHCS 6. Self-reported confidence did not significantly distinguish between the

participants in the aftercare/no aftercare or between the rehospitalization/no

rehospitalization groups. Overall, participants reported feeling “somewhat confident” to

“confident” in their ability to deal with a variety of events and symptoms following discharge from the hospital. The subscales of the MHCS were also explored for mean differences that might have occurred between the different rehospitalization and aftercare adherence groups. Results for the Optimism, Coping, and Advocacy subscales also showed no significant mean differences of subscale by outcome groups. Only the full

MHCS was used in further analysis.

CABS-Consumer Provider Subscale. There was no significant difference in mean scores on the consumer-provider subscale between the participants who did and did not

follow-up with aftercare, and participants who were rehospitalized and were not

rehospitalized. Overall, participants reported that they were “usually” satisfied with the

different aspects of the consumer-provider relationship (e.g., listening carefully to

consumers).

Motivational Beliefs, Rehospitalization, and Aftercare Adherence

TMQ-External. The external subscale of the TMQ revealed no significant

difference in mean scores on the consumer-provider subscale between the aftercare/no

6 MHCS question 5 “How confident are you now that you can boost your self-esteem?” was accidentally not included in the questionnaire booklet. The full scale MHCS is therefore comprised of 15 items and not

65 aftercare or between the rehospitalization/no rehospitalization groups. Low scores on this subscale reflected low perceived coercion to attend further treatment. The majority of participants rated it “slightly true” that their continued participation in treatment was at least partially due to external influences and pressures.

TMQ-Internal. As may be seen in Table E5, patients who did not follow-up with aftercare reported higher internalized motivation (M=4.22, SD=.59) than participants who did (M=3.94, SD=.58). This difference approached significance [t(72) = 1.97, p = .052].

While participants who did not follow-up with aftercare scored higher overall in internal motivation, these differences might be clinically negligible, as the majority of participants in both groups responded “mostly true” to statements reflecting internalized motivation (e.g., “It’s important to me personally to solve my problems”), reflecting good levels of self-reported internal motivation.

URICA. There were no significant differences in mean scores on the URICA readiness to change scores between the participants who did and did not follow-up with aftercare, and participants who were rehospitalized or not. The endorsed motivation of participants to change their behavior was relatively high in all groups.

Multivariate Analysis

Missing data. Prior to analysis, all variables were examined for accuracy of data entry, missing values and fit between their distributions, and assumptions for predicting aftercare adherence and hospital recidivism. Evaluation of expected frequencies for categorical socio-demographic clinical predictors had revealed the need to collapse several of these predictors to have sufficiently large cell sizes for subsequent analyses.

16 as in the original study and the Optimism subscale is comprised of 5 scale items instead of 6.

There was one missing data value on the URICA, CABS, and MHSIP, respectively.

These were replaced with the mean for all cases (Tabachnick & Fidell, 2001).

Predicting aftercare adherence. Inspection of the correlations among all variables and tests for multicollinearity (Field, 2005; Menard, 1995) did not reveal any significant problems of dependency between predictor variables. The bivariate correlation matrix was examined using Pearson’s correlation coefficient. No two variables had a correlation of .70 or above, which would be considered unacceptable (Fox, 1997). Multicollinearity was examined by using the collinearity statistic in the linear regression option of SPSS

(as recommended by Menard, 1995). This is acceptable for logistic regression, as the concern is with possible relationships among the independent variables and not with the functional form of the model on the dependent variable. Menard (1995) recommends that a tolerance of less than .20 is a cause for concern, and a tolerance of less than .10 almost certainly indicates a serious collinearity problem. Additionally, Field (1995) recommends checking the Variance Inflation Factors (VIF) that should be no larger than

10. All of the tolerance levels were above .2, and no VIF was larger than 3.5 for the variables in the model.

Violation of linearity between the logit of the dependent and each independent variable was assessed using the Box-Tidwell transformation (Menard, 1995). Although the transformation detected a violation of linearity in the logit for the DAI-10, a model in which it was replaced by its logarithm did not distinguish from the model with the untransformed value. Therefore the model with the untransformed values was analyzed

(Tabachnick & Fidell, 2001).

Model fit (goodness-of-fit) was assed using the Hosmer-Lemeshow test statistic.

This statistic is not significant if the model fits the data, which was observed for all the logistic regression models of aftercare adherence (see Table E7). While this statistic ascertains how well a particular model fits the data, it does not describe lack-of-fit, where the model fails. Regression diagnostics are therefore vital to assess the lack-of-fit.

Inspection of the standardized residuals revealed that the final logistic model produced four cases for which the model worked poorly. In an average, normally distributed sample, 95% of cases should have average values that fall within ±1.96, and 99% of cases should have values that lie within ±2.58. While the current model still meets these specifications, Field (2005) recommends that standardized residuals ± 3 are cause for concern, and that any standardized residuals close to ± 3 should be inspected. For the current sample, 2 of the 4 cases should be considered outliers, one with a residual of -

3.38, and one close to -3 (-2.72). Further analysis of cases that might have an unduly large influence on the model was conducted (Menard, 1995). The leverage statistic, which varies between 0 (no influence) and 1 (completely determined) the parameters in the model), showed no cases with unusually high hat values (2(K-1)/N or .44 for the current model). Cook’s distance is a measure of how much all the other residuals change when the ith observation is deleted from the analysis. Cook’s d > 1.0 identifies cases that might be influential. This statistic revealed that all four cases for which the model was poorly specified also had values of Cook’s distance >1.0. DBeta (called DFBeta in

SPSS) is Cook’s distance, standardized, and measures the change in the logit coefficients for a given variable when a case is dropped. None of the predictor variables had a

DFBeta with a value >1.0, which is the accepted cut-off point. The identified outliers

68 were retained in the current model, as individual level inspection of the data did not reveal a justification for their removal.

The first hypothesis tested the predictive validity of the HBM, a motivational model including the TTM, and a combination of the HBM and motivational model, to provide an explanatory framework for aftercare attendance versus no aftercare attendance in a 3-month follow-up period. A sequential logistic regression was performed through

SPSS Logistic Regression to assess prediction of aftercare appointment contact. The first step contained six empirically and theoretically derived demographic-clinical predictors, and the next step contained six attitudinal variables representing dimensions of the Health

Belief model. Finally, three variables representing a motivational decision-making model were added to the analysis. Socio-demographic-clinical predictors were gender

(male/female), race (African American/other), age, past aftercare noncompliance

(yes/no), having an assigned case manager (yes/no), and psychiatric diagnosis at discharge (schizophrenia versus depression/bipolar disorder). Other covariates of empirical and theoretical interest could not be evaluated due to the small sample size and associated problems with overfitting the data. Attitudinal predictors measuring participants’ health beliefs were psychiatric medication attitudes (DAI-10), awareness of illness (AII 1-6), intention to follow-up with outpatient care (AII-Question 7; scored by participants on a scale of 1-10), levels of confidence to deal with a variety of symptoms/problems following discharge from the hospital (MHCS), past aftercare consumer-provider relationships (CABS subscale), and past access to care (MHSIP subscale). Motivational constructs were readiness to change (URICA) and internal and external motivation (TMQ-internal, TMQ-external subscales).

The likelihood ratio test (sometimes called the goodness-of-fit or G) was used to test the difference between a given model and any nested model, which is a subset of a given model (Garson, 2006). Model chi-square is the difference in the likelihood ratios (-

2LL) for two models, and degrees of freedom is the difference in the degrees of freedom for two models. R-Square measures in logistic regression attempt to measure the strength of associations (variance explained), but should only be considered as approximations to

OLS R2 (Garson, 2006). Both the Cox and Snell’s R-Square, and the Nagelkerke’s R-

square (a correction of the Cox and Snell statistic to assure that the coefficient can vary

between 0-1) were examined.

Table E6 displays the sequential (hierarchical) regression results. Table E7

displays the model change statistics for the logistic regression results. The first block

containing socio-demographic and clinical risk variables (model 1) was significant (p

<.028), producing a Cox & Snell R2 of .18, and Nagelkerk R2 of .24. This represents a

significant change over a base model without any predictors. Variables testing the HBM

were entered on the second block. This addition did not produce a significant

improvement (p =.270) to model 1. Motivational variables were entered in addition to the risk variables in model 3; however, the addition of the motivation variable block also did not lead to a significant improvement in the model (p =.325). Model 4, the addition of the combination of the HMB variables and motivational variables to the risk variables, also did not provide a significant improvement over model 1 (p =.272).

Another approach to model evaluation compared predicted group membership

with observed group membership (Pample, 2000). The final model correctly classified

73.6% of the cases, compared to 56.9% of cases in the model containing only the

70 constant and none of the predictors. The sensitivity of the final model, correctly predicting aftercare contact when it occurred, was better than chance .65 (20/31), but was not overly impressive. The specificity--correct predictions of no aftercare contact when none occurred--was better at .80 (33/41). Inspection of the classification tables at the introduction of the risk variables revealed that these variables alone had good sensitivity

.85 (35/41), but the specificity fell around chance at .48 (15/31). The addition of the health belief variables improved specificity to .52 (16/31); however, this was due to the correct reclassification of only one case. The addition of the motivational variables to the model had the largest effect on specificity increasing it by .28. However, the addition of the third step also decreased sensitivity by .05, as compared to a model with risk variables, and a model with risk and health belief variables combined. Garson (2006) warned that classification tables are problematic as an indicator of goodness-of -fit because they ignore actual predicted probabilities, and instead use dichotomized predictions based in a cut-off (in these analyses .5). Therefore, the prediction does not reveal how close to 1 or 0 the predictions were. This can be visually inspected by looking at the classification plots provided as an option in SPSS (Garson, 2006). A U- shaped rather than normal distribution is desirable, as it indicates the distribution is well differentiated.

Figures F1and F2 show the classification plots for the model base on risk predictors alone and model 4. This table shows that the full model (model 4) improved the spread of the distribution of scores toward the endpoints, but also showed that a cluster of cases (both correctly and incorrectly classified) remained around the cut point.

This indicates a problematic model fit for difficult cases.

The null hypothesis that the addition of the HBM, motivational model including the TTM, and the combination of the HBM and motivational model did not significantly improve a basic risk factor model and could not be rejected.

The second hypothesis examined the likelihood that an individual's positive attitudes toward psychiatric medications, better insight into their illness, higher levels of either intrinsic or intrinsic-extrinsic motivation, higher readiness for change, better previous consumer-provider relationship satisfaction, better previous access to outpatient treatment, and higher levels of self-efficacy can significantly predict their aftercare appointment adherence.

Like the t-test in linear regression, the Wald statistic can be used to calculate if the b-coefficient of a predictor is significantly different from zero (Field, 2005). If it is, an assumption can be made that the predictor is making a significant contribution to the prediction of the outcome. Inspection of model 1 (Table E6) shows that case management (p =.008) significantly contributed to aftercare adherence. Because in logistic regression the interpretation of predictor b-coefficients lacks a meaningful metric, the odds ratio (represented by Exp (B)) is interpreted (Field, 2005). Having a case manager resulted in a 1.98 increase in the log odds of Aftercare (p = .008). Stated as an odds ratio, for participants with a case manager, the odds of aftercare attendance are 7.25 times as large as compared to participants without case management services (Exp (B) =

7.25, C.I.=1.68 – 31.31), while holding all other variables constant.

Inspection of model 2 (Table 6) did not reveal any significant results for any of the variables approximating the HBM. Model 3 (Table 6) does not show any significant results for the variables assessing motivational constructs. Model 4 (Table E6) shows

72 that the CABS consumer-provider subscale (p = .034) and the MHSIP access to care subscale (p = .048) significantly contributed to the final model, while holding all other

HMB, motivational, and risk variables constant. For every one unit increase on the

CABS consumer-provider rating scale, we expect a 1.49 increase in the log odds of

Aftercare (p = .034). Stated as an odds ratio, for the CABS consumer-provider subscale, the odds of aftercare attendance are 4.4 times as large for an additional one unit (score) increase on the CABS subscale score [(Exp (B) = 4.43, C.I.=1.12 – 17.55)], while holding all other variables constant. In other words, when examined in the full mode, the null hypothesis that there is no significant difference in odds of aftercare attendance in the presence of better past consumer-provider relationship as measured by the CABS can be rejected. For every one unit (score) increase on the MHSIP access to care subscale, we expect a 1.12 increase in the log odds of Aftercare (p = .049). Stated as an odds ratio, for the MHSIP access to scare subscale, the odds of aftercare attendance are 3.1 as large for an additional one unit (score) increase on the MHSIP subscale score [(Exp (B) = 3.05,

C.I.= 1.01 – 9.23)], while holding all other variables constant. In other words, when examined in the full model, the null hypothesis that there is no significant difference in the odds of aftercare attendance in the presence of better past access to outpatient care as measured by the CABS can be rejected.

For both of these variables the confidence intervals did not contain 1; however, these intervals are rather large. This indicates that the strength of association between the predictors and the likelihood of aftercare attendance is not very precise, most likely due to the small sample size. Individuals’ positive attitudes toward psychiatric medications, better insight into their illness, positive ratings of future outpatient attendance, readiness

73 for change, higher levels of either intrinsic or intrinsic-extrinsic motivation, and higher levels of self-efficacy did not significantly predict the likelihood of aftercare adherence.

Predicting Rehospitalization

Inspection of the correlations among all variables, and diagnostic tests for

multicollinearity, were assessed in the same fashion described earlier. No significant

problems of dependency existed between predictor variables. The bivariate correlation

matrix was examined, in addition to utilizing the collinearity statistic in the linear

regression option of SPSS. All of the tolerance levels were above .2, and no VIF was

larger than 3.5 for the variables in the model, indicating the assumption of no

multicollinearity.

Violation of linearity between the logit of the dependent and each independent variable was assessed using the Box-Tidwell transformation (Menard, 1995). Although the transformation detected a violation of linearity in the logit for the number of previous hospitalizations variable, a model in which it was replaced by its logarithm did not differ substantially from the model with the untransformed value. Therefore, the model with

the untransformed values was analyzed (Tabachnick & Fidell, 2001). Inspection of the

standardized residuals revealed that the final logistic model produced four cases for

which the model worked poorly. For the current sample, one case should be considered

an outlier with a standardized residual of 2.83. Additionally, two cases had standardized

residuals of 2.59 and 2.57. One case had a standardized residual of -2.14. This indicates

that the model has 5.6% of cases that lie outside ±1.96, and 2.8% cases that lie outside of

±2.58. Especially the latter results indicate that the level of error within the model might

be problematic, and the model is a limited representation of the data. Furthermore,

74 casewise diagnostics of the leverage statistic showed that eight cases had mildly elevated hat values (2(K-1)/N or .42 for the current model); however, all values remained within acceptable limits (Field, 2005). Cook’s distance revealed that the all four cases for which the model was poorly specified also had values of Cook’s distance >1.0. There were three additional cases with Cook’s distance >1.0, the largest value 1.57. One case had a DFBeta with a value >1.0 on the Awareness of Illness variables (Dfbeta 1.44), indicating that this case had a large influence on the logit coefficient of this variable.

The third hypothesis assessed the predictive validity utilizing the HBM, and/or a motivational model including the TTM, to provide an explanatory framework for hospital recidivism in a 3-month follow-up period. A sequential logistic regression was performed through SPSS Logistic Regression to assess prediction of rehospitalization.

The first step contained six empirically and theoretically derived demographic-clinical predictors, the next step contained five attitudinal variables representing dimensions of the HBM, and finally three variables representing a motivational decision-making model were added to the analysis. Socio-demographic-clinical predictors were gender

(male/female), race (African American or other), age, prior number of psychiatric hospitalizations (count), having an assigned case manager (yes/no), and psychiatric diagnosis at discharge (versus depression/bipolar disorder). Attitudinal predictors measuring participants’ health beliefs were psychiatric medication attitudes (DAI-10), awareness of illness (AII 1-6), levels of confidence to deal with a variety of symptoms/problems following discharge from the hospital (MHCS), past aftercare consumer-provider relationships (CABS subscale), and past access to care (MHSIP

75 subscale). Motivational constructs were readiness to change (URICA), and internal and external motivation (TMQ-internal, TMQ-external subscales).

The same statistical tests were utilized for the analysis of aftercare adherence;

Table 8 displays the sequential (hierarchical) regression results. The first block containing socio-demographic and clinical variables was significant (p =.012), producing a Cox and Snell R2 of .21, and Nagelkerk R2 of .30 (Table E9). This represented a

significant change over a base model without any predictors. Variables testing the Health

Belief Model were entered on the second block. The addition of this block was significant (p =.026) and the health belief variables also produced a significant improvement (p =.002) in model 2 as compared to model 1 in the prediction of hospital recidivism. Model 2 produced a Cox and Snell R2 of .34, and Nagelkerk R2 of .49.

Model 3 added all motivational predictors to the risk variables. This block also did not lead to a significant improvement in the model (p =.161), but resulted in a significant (p

=.045) Hosmer-Lemeshow test statistic, indicating that model 3 was a poor fit for the actual data (Table E9). Finally, risk, health belief, and motivational variables were tested in model 4. In comparison with a model containing only the risk variables, the addition of the HBM and motivational predictors was significant (p =.025). The full model

(Model 4) remained statistically significant (p =.002) (Table E9).

Predicted group membership was compared with observed group membership

(Pample, 2000). Overall, the final model correctly classified 87.3% of the cases, compared to 73.2% of cases in the model containing only the constant, and none of the

predictors. The sensitivity of the final model, correctly predicting rehospitalization when

it occurred, was better than chance .68 (13/19), but not overly impressive. The specificity

76 was better at .94 (49/52). Inspection of the classification tables at the introduction of the socio-demographic variables revealed that these variables alone had good sensitivity at

.94, but the specificity was less accurate than chance at .42 (8/19). The addition of the health belief variables improved specificity to .53 (10/19), and the addition of the motivational variables to the model further increased specificity to .68 (13/19) by correctly reclassifying three cases. Inspection of the classification plots (Figure 3-5) shows that the full model improved the spread of the distribution of score toward the endpoints, but also shows that a cluster of cases (both correctly and incorrectly classified) remained around the cut point. This indicates a problematic model fit for difficult cases.

The null hypothesis that the addition of the HBM did not significantly improve a basic risk factor model in the prediction of hospital recidivism can be rejected. The null hypothesis that the addition of the motivational model did not significantly improve a basic risk factor model in the prediction of hospital recidivism could not be rejected.

The null hypothesis that the addition of the combined HBM and motivational model did not significantly improve a basic risk factor model in the prediction of hospital recidivism can be rejected.

The fourth hypothesis examined the likelihood that individuals’ positive attitudes toward psychiatric medications, better insight into their illness, higher levels of either intrinsic or intrinsic-extrinsic motivation, higher readiness for change, better previous consumer-provider relationship satisfaction, better previous access to outpatient treatment, and higher levels of self-efficacy can significantly predict hospital recidivism in a 3-month follow-up period. Like the t-test in linear regression, the Wald statistic calculated if the b-coefficient of a predictor is significantly different from zero (Field,

2005). Inspection of model 1 (Table E8) shows that number of previous hospitalizations significantly contributed to rehospitalization. For number of previous hospitalizations, each additional hospitalization resulted in a .17 increase in the log odds of rehospitalization (p = .024). Stated as an odds ratio, for a participant with an additional previous hospitalization, the odds in favor of being rehospitalized are 1.18 times as large as for participants with one less prior hospitalization, (Exp (B) = 1.18, C.I.=1.03 – 1.37), while holding all other variables constant.

Inspection of model 2 (Table E8) did reveal that the HBM construct of perceived benefits, as measured by the DAI-10, significantly decreased the likelihood of hospital recidivism. For every one unit increase on the DAI-10 (better attitudes) we expect a .34 decrease in the log odds of hospital recidivism (p = .006). Stated as an odds ratio, for the

DAI-10, the odds of hospital recidivism are .71 for participants with each additional one unit (score) increase on the DAI-10 (Exp (B) = 4.43, C.I.=1.12 – 17.55), while holding all other variables constant. In other words, when examined in model 2, the null hypothesis that there is no significant difference in the odds of hospital recidivism in the presence of better psychiatric medication attitudes as measure by the DAI-10, can be rejected. In the presence of the variables approximating the HBM, the predictive validity of number of prior hospitalizations on hospital recidivism became non-significant. The risk factor of discharge diagnosis did become significant (p =.044) and gender approached significance

(p =.056). For participants with schizophrenia, the odds of rehospitalization are 9.59 times as large, as compared to participants with either bipolar disorders or depressive disorders, while holding all other risk and HBM variables constant.

Model 3 (Table E8) does not show any significant results for the variables assessing motivational constructs. Model 4 (Table E8) shows that each of the following variables significantly and independently predict hospital recidivism: (a) gender (p

=.040), (b) discharge diagnosis (p =.041), and (c) DAI-10 (p =.026), while holding all other risk, health belief, and motivational variables constant. For every one unit (score) increase on the DAI we expect a -.381 decrease in the log odds of rehospitalization.

Having a one unit higher score on the DAI-10 yielded an odds ratio of .683 (Exp (B) =

.683, C.I.= .489 – .956), while holding all other variables constant. Being male decreased the odds of being rehospitalized for the sample. The odds ratio against rehospitalization are .055 times lower for males as compared to females (Exp (B) = .055,

C.I.=.003 - .882). In other words, the odds of a female being rehospitalized are 18.18

(inverse odds ratio) times as large compared to men, while holding all other variables constant. Having a schizophrenia spectrum diagnosis versus depression/bipolar spectrum disorders yielded an odds ratio of 15.41 for rehospitalization (Exp (B) = 15.41, C.I.=1.12

– 212.04), holding all other variables constant.

For the three significant variables the confidence intervals did not contain 1.

However, the intervals, especially for the diagnosis variable, are rather large. This indicates that the predictive validity of the predictors and the outcome is not very precise, most likely due to the small sample size. Individuals’ better insight into their illness, past positive consumer-provider relationships, past positive access to outpatient care, readiness for change, higher levels of either intrinsic or intrinsic-extrinsic motivation, and higher levels of self-efficacy did not significantly predict the likelihood of aftercare adherence.

CHAPTER 4: DISCUSSION

The objective of the study was to investigate which theoretical model (HBM and/or TTM), as well as which socio-demographic, clinical, and systemic risk factors, would best predict two mental health outcomes (aftercare contact and rehospitalization).

The models for each outcome variable were tested separately.

Aftercare Adherence

Community treatment nonadherence following hospitalization has been identified as an increasing problem in the context of managed care when appointments are missed without cancellation, resulting in long waiting lists, wasted staff resources, and staff frustration. The current study found that approximately 58% of referred study participants

(appointment or a walk-in clinic) did not have any aftercare contact within a 3-month period following hospitalization discharge. Given the high cost of providing inpatient care, and the increased risk for psychiatric relapse without aftercare that can lead to further inpatient admission, successful linkage to aftercare is especially important.

However, while this number appears high, it should be noted that these finding are consistent with rates of aftercare treatment non-adherence for some medical conditions.

It further underscores that lower aftercare treatment adherence is usual and expected in individuals with longer term mental health problems, especially after an acute exacerbation of symptoms has been treated.

This study used theoretically derived variables approximating health care decision- making models such as the HBM and the TTM, testing these theories to examine their usefulness in understanding and predicting aftercare treatment adherence.

It also used socio-demographic, clinical, and system variables as risk factors for nonadherence.

Risk factors. Past research suggesting that socio-demographic and clinical characteristics are not reliably associated with aftercare adherence (Klinkenberg &

Calsyn, 1996) was supported by the findings of the present study, in which race, gender, and age were not associated with the outcome in the multivariate analysis. A recent study by Compton et al. (2006) also did not find that socio-demographic characteristics were related to aftercare appointment adherence following hospital discharge. However, in studies published since the 1996 review article by Klinkenbery and Calsyn, both

Compton et al. (2006) and Boyer et al. (2000) have found that involuntary legal status upon discharge or leaving against medical advice was independently and significantly related to aftercare nonadherence. Compton et al. (2006, p. 535) hypothesized that this finding supported previous finding that lack of insight “if involuntary legal status at discharge is taken as a proxy of insight” was predictive of aftercare nonadherence. The current study was not able to utilize involuntary admission status as the psychiatric facility has a very low rate of involuntary admissions, and only study participants had been involuntarily admitted. Of interest is the finding of the current study that a similar percentage of individuals were non-adherent to their initial aftercare appointments (58%) as compared to the Compton et al. study participants (64%). This indicates that while involuntary admission status is an important predictor that deserves further research and clinical attention, other factors, for example, system or linkage problems, may play a larger role for aftercare nonadherence in voluntary patients. The current study did not find previous aftercare non-compliance (hypothesized to be a strong predictor of future

81 nonadherence) and discharge diagnoses to be associated with participants’ aftercare contact likelihood.

Different explanations for the phenomena that past behavior does not always

predict future behavior have been suggested (see Ajzen, 2002a). Additionally, the lack of

this relationship in the current study might have been a methodological artifact. For

instance, the dichotomized variable (yes/no) was coded from participants’ charts, which

contained few details as to the frequency, recency, course, or types of nonadherence.

Also, the sources of this information differed (obtained by clinicians from patients’ self-

report, or behavioral health agency information) between study participants. Therefore,

the actual previous aftercare nonadherence captured by this variable may have been

insufficiently reliable to predict future aftercare treatment contacts.

Having a case manager was the only significantly predictive variable of aftercare

adherence on the first block of the logistic regression analysis. Holding other variables

on this block constant, the presence of case management significantly increased the odds

of aftercare adherence. However, this result is at least partly a function of the operationalization of aftercare adherence, which was coded as ‘yes’ when a case management contact occurred in the 3-month follow-up period. For 5 of 15 participants with intensive case management (ICM) services, contact with this service was the only aftercare service contact billed in the follow-up period. In order to examine if intensive case management is predictive in linking individuals to other aftercare services such as outpatient psychiatry appointments, individuals with solely case management contact in the follow-up period should be removed from data analyses in future studies. Due to the limited sample size in the current study, these 5 cases were not removed from the

82 aftercare analyses. Two additional participants had a combination of ICM plus another type of service contact. However, having a case manager prior to hospitalization did not necessarily result in a case management follow-up contact, as 4 of 15 individuals did not have any outpatient aftercare contacts during the follow-up period. Finally, three individuals of this group had an outpatient service contact, but no case management service contact. These findings limit the applicability of case management services as a predictor for aftercare adherence. At best, the findings indicate that in the current study, case management was predictive because it largely predicted re-connection with this contact and/or possibly connection to another service (5/15 participants and 2/15 participants).

In one of the most recent and influential investigations on aftercare adherence and continuity of care, Boyer et al. (2000) showed that other system variables in the form of interventions aimed at enhancing linkage significantly increased the likelihood of aftercare attendance. Boyer et al’s linkage strategies could not be investigated in the current study, as 3 of their 4 strategies (patient met outpatient clinician/ staff before discharge; patient started outpatient program before discharge; patient visited outpatient program before discharge) were not practiced in the hospital where the current research was conducted. The fourth linkage strategy (discharge plans discussed with outpatient clinicians/staff such as intensive case manager) was not consistently documented in patients’ charts. Compton et al. (2006) also pointed out that they were unable to study such “support service” as reminder telephone calls prior to the first aftercare appointment, as these services were not provided in their research setting. They did however, find it significant that not having an established outpatient clinician

83 significantly and independently predicted aftercare appointment nonadherence. This finding points to the importance of having a mental health system that is able to provide continuity of care, which was also pointed out in the literature review by Klinkenberg and

Calsyn (1996).

Motivational variables. Boyer et al. speculated that the motivation of their participants may have been an important unmeasured variable that might have contributed to their participants’ completion of referrals in addition to the system variables, which were the focus of their investigation. Motivational constructs and readiness for change were directly assessed in the current study, but the addition of these constructs to the risk model did not yield model improvement to predict aftercare adherence. In the current study, participants with greater expressed readiness to change, as measure with the URICA, did not have improved odds of an aftercare contact following hospital discharge. The other two variables that were used to test motivational constructs were internalized and externalized motivation, as measured by the TMQ. Both types of motivation also failed to account for aftercare adherence. While the finding that motivation was not predictive for aftercare adherence seems counterintuitive, it lends some support to Boyer et al.’s findings that system interventions, such as intensified linkage strategies, might indeed account for a significant variance in completed aftercare referrals independent of motivational constructs.

Blanchard et al. (2003) and Pantalon and Swanson (2003) also failed to find that the continuous URICA readiness scores predicted treatment outcomes in their respective samples of substance abusers and dually diagnosed study participants. However, three alternative sets of explanations for the counterintuitive lack of predictive validity of

84 motivational measures on behavioral outcomes should be considered before dismissing motivational models of behavior change. First, as both Blanchard et al. and Pantalon and

Swanson have pointed out, attitudes regarding motivation to change are not synonymous with behavioral intentions or actual plans to change. For example, as suggested by

Blanchard et al. (2003), the URICA assesses hopes and desires for change, rather than actual plans to change. However, the failure of the TMQ’s internalized motivation subscale to predict aftercare adherence is important with regard to this hypothesis, because it assessed attitudes toward behavior change more directly than did the URICA.

Therefore, the lack of predictive validity cannot be reduced to the construct validity of the measurement instrument, but rather to the observation that behavioral intent does not easily translate into actual change.

The second and third sets of explanations more directly address the latter phenomena, as they pertain to the stability of the construct of motivation and the associated timing of the measurement administration. In the current study, these attitudes were measured at the end of one treatment (inpatient), and overall participants expressed relatively high levels of motivational readiness M=10.56, SD=1.46 (this score can vary from -2 +14; Carbonari, DiClemente, & Zweben, 1994). As Pantalon and Swanson

(2003) speculated, high readiness scores might actually reflect a type of “overconfidence” or belief that one has already changed problematic behaviors, hence resulting in lower adherence rates, which may account for the current findings. Alternatively, the lack of an association between high motivational readiness and actual adherence behavior may suggest that while motivational attitudes were genuinely high at the time just prior to discharge (as would be expected), once discharged and confronted with the

85 responsibilities and pressures of their community lives, motivational readiness changed before the aftercare contact was completed. This supports the important notion that the construct of motivational readiness is fluid and may not predict behavior over time and intervening events, as was also suggested by Blanchard et al. (2003). Model of motivation might therefore not be suited in prediction studies of future decision making.

However, motivational constructs might continue to hold promise in the study of behavior change or health care decision making, if, for example, they are measured closer to the decision making time point, and if the complexity of this construct is adequately captured and measured. For example, secondary correlational analysis of the relationship between the continuous readiness score and the two TMQ subscales indicated that the

URICA readiness scores were significantly positively correlated with the internalized motivation subscale scores, but were not negatively correlated with the externalized subscale, as was observed by Ryan et al. (1995).

The findings of the current study supported the notion that motivation is not a unitary construct. However, as very few participants in the current study reported high external motivation, the statistical power to detect any significant effect of this variable was extremely limited. This variable should be investigated further in a setting in which individuals may perceive greater external coercion (e.g., involuntary hospitalization).

Future research should also consider the assumed temporal stability of internal motivation and investigate its function as a mediator or moderator variable for aftercare adherence. For example, differences in levels of internal motivation at discharge may explain differences in aftercare attendance, if these appointments all occur soon after discharge, or if length of time between discharge and first appointment can be included as

86 a risk variable. A recent study found that as time between the aftercare appointments and discharge date increased, there was a significantly increased likelihood of missed aftercare appointments (Compton et al., 2006).

Health belief variables. Variables modeled on the Health Belief Model also failed to provide an explanatory framework to explain aftercare treatment adherence.

Adams & Scott (2000) used this model to predict medication adherence in several mental disorders, and hypothesized that the study’s findings might have been partly attributable to using reliable and validated measures to approximate the HBM. DeHart and Birkimer

(1997) pointed out that in studies on health decision making (for example in the study on safe sex practices) the constructs of the Health Belief Model are either not assessed with established psychometric instruments to assess necessary constructs, or with rigorous scientific techniques for item-pool derivation to capture the HBM constructs for the respective health decision making domain. Similarly to the Adams & Scott study, the current research utilized reliable and valid instruments to approximate the HBM. The lack of predictive findings is likely not a primary function of any shortcoming in the psychometric properties of the selected instruments, but rather related to the choice of the underlying variables to predict aftercare adherence. None of the instruments were developed specifically to measure HBM constructs in relation to aftercare adherence decision making, and no such measures could be located in the literature. The selected variables therefore function as “proxy” variables for the different constructs of the HBM, and some of these variables may not have fully captured the different constructs of the

HBM as hypothesized.

For example, perceived susceptibility, or patients’ beliefs regarding having a mental illness, was measured by the Awareness of Illness Interview (AII). The AII contains two subsets of questions addressing illness recognition and perceived need for treatment. Perceived need for treatment was conceptualized as a proxy variable for the

HMB construct of perceived severity of illness. As the AII only provides one composite score, low AII scores (indicating high awareness) would suggest that a participant was aware of the severity of her illness and perceived herself to need follow-up care.

However, the AII does not implicitly assess an individual’s consideration of the clinical consequences for failing to attend outpatient care (e.g., “If I do not attend aftercare services I will experience a relapse of my mental illness symptoms”), or the possible social consequences of the health condition, which are also part of the perceived severity of the illness construct. Therefore a possible shortcoming of the present study was that the perceived severity dimension was not adequately measured. However, no reliable and valid instrument that measures severity of illness from the consumer’s perspective could be located for the current study, and the development of such an instrument may be useful for future research studies of behavioral health care decision making.

A second limitation in the selection of variables to approximate the HBM occurred with the construct of perceived barriers to treatment (the monetary and psychological costs of the advised action). This construct was assessed with two subscales of the CABS and MHSIP, both instruments widely used in assessments of consumer opinions of their behavioral health care provision in the public sector.

However, these instruments have had limited use in the prediction of consumer behavior, and instead, have primarily functioned as quality indicators of consumer satisfaction with

88 available services. In the current study, higher scores (indicating more positive rating) on the CABS consumer-provider relationship subscale was the only variable that approached significance (p =.07) in the combined model of risk factors and the variables approximating the HBM. It is likely that with additional power this variable might have reached significant predictive validity. A recent meta-analysis (Martin et al., 2000) indicated that the overall effect size of therapeutic/working alliance in relation to therapy outcome (for example, symptom improvement) is moderate but consistent, regardless of many of the variables that have been posited to influence this relationship.

While the current findings are promising and should be investigated in future research, some important changes in the administration of the CABS subscale may nevertheless be required to more reliably assess the importance of this variable on aftercare adherence. With aftercare adherence as the health care decision of interest, the questions on the CABS subscale were asked in reference to participants’ last outpatient experience, rather than relationships experienced during the index hospitalization. Given that the times of last outpatient services and the nature of these services varied widely between participants, this might have affected participants’ recall, and additionally, limited the predictive validity of this variable. It is also unclear whether a negative experience with one provider would have the same effect on a referral back to the same provider rather than a different one. Data on this point collected in the present study could not be analyzed due to the relatively small sample size and missing information.

Several participants were unable to recall the name of their past outpatient provider, and the name of a discharged participant’s referral locations was not consistently documented in patients’ charts. Future research should use additional or more reliable data sources to

89 establish the last outpatient care provider, and control for the effects of longer elapsed time periods since last outpatient service provision.

Access to care, measured with the MHSIP subscale, was not significantly related to future aftercare adherence, even though the relationship was in the hypothesized direction. Like the CABS, the MHSIP subscale assessed past access to outpatient care, and therefore, similar methodological problems. For example, it is possible that individuals with negative access to care experiences at one provider may have decided not to return to this provider, but attended an appointment at a different provider after hospitalization discharge. The importance of previous access to care experience on future aftercare adherence may also be related to the availability of different service providers in a community. Negative past access to care may have a larger impact in environments where there are fewer provider alternatives than there were in the current study. This might be an important variable to examine in future studies.

The construct of perceived benefits (e.g., being symptom-free, having improved functioning), approximated by measuring participants’ beliefs about psychiatric medications, was not related to aftercare adherence. This finding was somewhat surprising, as this variable was selected under the assumption that most patients would be discharged with a psychiatric medication prescription. Additionally, nonadherence to psychiatric medication has been widely studied (Perkins, 2002; Scott, 2000; Weiden &

Glazer, 1997; Weiden, Olfson, & Essock, 1997) and empirically linked to outcomes such as illness relapse and rehospitalization. Interestingly, negative attitudes toward medications significantly increased the likelihood of rehospitalization, but did not appear to be predictive of aftercare adherence. It is possible that participants with negative

90 attitudes toward medications may not have adhered to their prescribed medication regimen, but still attended at least one aftercare service contact, and at some point experienced a rehospitalization. The present design did not allow the direct measurement of adherence to psychiatric medication following discharge from hospitalization. Future research should continue to address this theoretically and empirically important variable, for example, by investigating actual medication adherence in the community and its relationship to aftercare attendance.

The construct of self-efficacy was measure by using the Mental Health

Confidence Scale (MHCS), which is based on Bandura’s self-efficacy theory and qualitative research on self help (Carpinello et al., 2000). In the current study, high self- reported levels of self-efficacy were not associated with improved odds of aftercare service utilization. The lack of predictive validity of this dynamic variable is similarly counterintuitive, as is the failure of the motivational constructs to predict aftercare adherence discussed previously. However, while dynamic variables have been shown to be important in explaining additional variance in the prediction of a variety of behaviors

(such as in violence prediction, sexual re-offending), they are by definition more context and situation specific as compared to static variables, and therefore difficult to measure due to their time sensitivity. Similar to motivational constructs, the construct of confidence/self-efficacy might be fluid, and affected by numerous contextual variables that a participant encounters once discharged after the index hospitalization. High self- efficacy implies a belief that one can carry out a given behavior. An alternative explanation for the current findings is that self-efficacy is a relatively stable construct, but that high levels of confidence (being generally optimistic about the future, being able to

91 deal with negative symptoms or events and advocate for one’s rights) may actually lead to decreased aftercare adherence as a result of “overconfidence.” The MHCS probably measured participants’ confidence levels at their highest levels (very close to discharge), so they might be more vulnerable to such “overconfidence” than at other times. It is conceivable that improvement in the operationalization of the constructs of the HBM, correction of measurement problems, and an increase in sample size might result in an improvement of predictive validity of this model for aftercare adherence. One of the main criticism of the HBM has pointed out that there is a lack of uniformity in testing the model and in the ways its constructs are operationalized (Champion, 1984). However, it is also equally possible and probably more likely that this model, even with improvements, is only partially adequate in describing and capturing healthcare decision making. The second most common criticism leveled at the HBM (which can also be leveled at most other social cognitive model of decision making), is that it leaves out other important variables that have been shown to be correlated of health care decision making. These variables are other personal variables such as past experiences, social support, socioeconomic status, and cultural and institutional variables such as public policy, health care disparities, and poverty to name but a few.

Clinical Implications

The current findings appear to indicate that the recent interest in applying the

TTM to dually diagnosed or primarily psychiatric population might be premature. While it is intuitively compelling to believe that motivation to engage with future treatment is predictive of aftercare adherence, this was not found in the current study. Several alternative explanations for these findings were provided in the discussion. Given that

92 the current study participants had relatively high URICA readiness to change scores and high internal motivation, it is still feasible that motivation interviewing can be a viable and effective clinical intervention. The current study suggests that motivational interviewing should not be used with all psychiatric inpatients. However, it might be beneficial to a subset of clients, for example, those with currently active substance abuse problems, or initially low levels of motivation. Further research is needed to examine the utility of these therapeutic approaches with specific populations. The current study did not find that variables approximating the health belief model were predictive of aftercare adherence. For example, the current research assessed awareness of insight and did not find it predictive of aftercare adherence. However, Compton et al. conceptualized involuntary admission status as a proxy variable for lack of insight, and several recent studies (Compton et al., 2006; Boyer et al., 2000) have found that involuntary admission status is related to aftercare nonadherence. This might have important implications for clinical interventions offered to patients during their inpatient stay. For example, in an investigation of compliance therapy, Kemp et al. (1996) found that study participants who received the intervention showed improved insight, attitudes, and adherence to psychiatric medication regimens, as compared to a control group.

Hospital Recidivism

The second aim of the present research was to develop a theory-driven model of rehospitalization. The current study found that approximately 27% of study participants were rehospitalized at least once within 3 months following hospital discharge. This is comparable to figures generally provided for 6-month hospital recidivism rates, estimated to be at 30% to 40% (Montgomery & Kirkpatrick, 2002).

Of these individuals, approximately 65% also had at least one type of aftercare service contact during follow-up. As the service utilization data did not allow a clear determination of the order of these services, it is possible that some of the aftercare contacts occurred after a second hospitalization. Receipt of at least one aftercare service contact has been shown to reduce rehospitalization risk, although the intensity of aftercare services has not (Klinkenberg & Calsyn, 1996). Referral to aftercare (not actual receipt of services) has also been shown to increase the risk of rehospitalization

(Thompson, Neighbors, Munday, & Trierweiler, 2003). The current study investigated socio-demographic, clinical, and system variables as risk factors for rehospitalization.

Risk factors. Klinkenberg and Calsyn (1996) reported that the only socio- demographic and clinical characteristics significantly associated with rehospitalization were the number of previous psychiatric hospital admissions and poor medication compliance. This is consistent with the findings in the current study, where race, gender, and age were all unrelated to aftercare services. The system/clinical variables of having a case manager and discharge diagnoses were also not significant predictors of hospital recidivism. Receipt of case management has been shown in several studies (e.g.,

Solomon, 1992) to be more effective than conventional aftercare in reducing rehospitalization.

However, it has also been shown that assertive case management services are more effective in reducing hospital recidivism than are other case management services

(Bond, Miller, & Krumwied, 1988). Due to the small number of participants with any type of case management, the different types of case management could not be analyzed separately. As only a minority of participants reported any case management services,

94 the statistical power to detect significant effects of this variable were limited. Inspection of the data revealed that 7 of the 15 participants with case management services were rehospitalized during the 3-month follow-up period. This could have occurred because case management may also function as a proxy variable for illness severity (individuals who qualify for case management services usually have more acute and/or severe illnesses). While case management might reduce the rate of rehospitalization for this group, the extent of this reduction might not place them below the rate of patients without case management (but also with less severe illnesses). This question cannot be properly answered without random assignment of case management to groups, something that would be very difficult to implement as part of a research design.

Participants’ prior number of hospitalizations significantly predicted rehospitalization, a finding that has been seen in other studies and has been summarized by Klinkenberg and Calsyn (1996). Each additional previous hospitalization increased the odds ratio for rehospitalization by approximately 18% (confidence interval 2%-37%), when the other risk variables were held constant.

However, when the HBM model was added to the risk factor model, several changes in the risk variables were observed. The previously significant variable of number of prior hospitalizations became non-significant at p =.069, the gender variable almost reached significance at p =.056 (with women being more likely to be rehospitalized), and discharge diagnosis became significant at p =.044 (individuals with a discharge diagnosis of schizophrenia being more likely to be rehospitalized), while holding all other risk variables and HBM variables constant. While some of these results are intriguing, there is no obvious theoretical reason why the presence of the HBM

95 resulted in these changes. Neither gender nor discharge diagnoses were independently related to rehospitalization, nor were they independently predictive in the basic risk model. Therefore these findings are not considered very robust, and will not be further discussed.

The discussion section on aftercare adherence outlined the reasons why several

other system variables (such as different linkage strategies) were not included in the present model of rehospitalization.

Motivational variables. Adding motivational constructs and readiness for change

to the risk model did not yield improvement in predicting hospital recidivism. The

motivational attitudes utilized in the current study were primarily geared toward

assessing participants’ motivational readiness to change and internal/external motivation

to continue with treatment. High readiness, high internal motivation, and high internal

and external motivation were predicted to be positively related to receipt of an aftercare

service contact and negatively related to rehospitalization. The failure to find a

relationship between motivation factors and hospital recidivism is consistent with the

constructs’ failures to predict aftercare adherence in the current sample.

Health belief variables. Variables modeled on the Health Belief Model did

provide some explanation for hospital recidivism. This model has not been previously

used to predict rehospitalization in psychiatric or dually diagnosed populations. Of the

components of the Health Belief Model, only the perceived benefits construct was

significantly predictive of rehospitalization. This construct was approximated by

exploring participants’ beliefs about psychiatric medications. Scores on the Drug

Attitude Inventory (DAI-10) were significantly associated with rehospitalization, while

96 holding all other variables of the combined risk and Health Belief Model constant. For every 1-unit increase on the DAI-10 (representing improved attitudes and beliefs about psychiatric medications) the odds ratio of rehospitalization decreased by approximately

30%.

Patients’ acceptance or rejection of prescribed medications is one of the strongest predictors of these treatments’ effectiveness (Fenton et al., 1997). In addition, medication non-compliance and partial medication adherence have been some of the most robust findings cited in the literature on hospital recidivism (Weiden, Kozma,

Grogg, & Locklear, 2004). Nonadherence to psychiatric medications has been especially scrutinized in schizophrenia research, as nonadherence with prescribed psychotropic medication accounts for 40% - 55% of all psychiatric rehospitalizations and relapses into active illness states (Adams & Scott, 2000; Perkins, 2002; Weiden, Olfson & Essock,

1997). The current study suggests that patient-related factors such as attitudes toward medications may also be predictive of hospital recidivism, consistent with previous findings on this point (Kemp et al.,1996). None of the other constructs measured by the

Health Belief Model were significantly associated with rehospitalization in the follow-up period. Insight, the variable chosen to approximate perceived susceptibility, has been cited as a significant predictor of medication nonadherence and rehospitalization (Cuffle et al., 1996; Olfson, Marcus, Wilk, & West, 2006). However, the current study used the

AII in a heterogeneous sample of participants that included individuals with depressive and bi-polar spectrum diagnosis, whereas in the past this variable has been primarily utilized with study participants diagnosed with schizophrenia spectrum disorders. A recent study by Pini et al. (2003) indicated that awareness of illness might be

97 differentially predictive, not only by general diagnostic categories, but also by subtypes of diagnostic dimensions. In addition, recent research by Lysacker, Campbell, and

Johannesen (2005) suggests that for individuals with schizophrenia, acceptance of mental illness might be interpreted in two very different ways, which might then relate to approaches taken to manage this illness. These researchers found that for individuals with high hope, insight of illness was associated with better adaptive coping skills, whereas high insight and lower hope led to significantly poorer coping. Given these findings, illness awareness might not exert a direct relationship on rehospitalization or even treatment adherence, as its effect might be moderated by other variables, such as high or low hope or optimism. Interaction effects of this variable should be studied in future research, for example with self-efficacy and/or perceptions of stigma, as poor insight has been conceptualized as an adaptive coping response to avoid stigmatization

(Corrigan & Penn, 1999).

Similar to the findings on aftercare contacts, the construct of self-efficacy as measured by the Mental Health Confidence Scale (MHCS) was not related to rehospitalization. Only one study could be located that investigated self-efficacy as one of the factors associated with rehospitalization among veterans in a substance abuse treatment program, a large portion of the sample with co-occurring disorders (Benda,

2002). This study did find a significant effect for self-efficacy on rehospitalization.

However, the follow-up time period was two years, and it is therefore possible that the effects of low self-efficacy did not manifest within the rather short follow-up time frame in the current study.

If indeed high levels of self-efficacy are a measure of “overconfidence,” in this study, the rehospitalized group had a slightly higher (but non-significant) mean group score on the MHCS as compared to participants not rehospitalized. The speculation that individuals low in motivation might be more aftercare-adherent because they might be more “realistic” in their appraisals that they require help to successfully negotiate change, was proposed by Pantalon and Swanson (2003). This rationale can easily be extended to the notion of self-efficacy, with a low belief in one’s own abilities to withstand adversity/relapse/negative life events possibly relating to greater perceived need or willingness to seek aftercare services. This hypothesis should be more carefully addressed in future research (e.g., Is this truer for individuals with depression versus individuals with schizophrenia, and by extension is this related to the construct of insight?), and with a larger sample size.

Clinical Implications

The finding that attitudes toward psychiatric medications have utility in the prediction of hospital recidivism has important clinical implications. Attitudes toward psychiatric medications should be explored as routine practice in routine assessments by clinical hospital staff. The existence of a therapeutic environment, where attitudes toward medications can be discussed openly, is important to facilitate a working relationship and thus a negotiated approach toward medication adherence with a patient.

The Substance Abuse and Mental Health Services Administration (SAMHSA, 2004) has produced a package for public dissemination, Medication Management Approaches in

Psychiatry, intended to enhance collaborative pharmacotherapy. In line with guiding principles of recovery and empowerment, patients should have access to thorough and

99 individualized medication education, to groups that address psychological and/or cultural issues such as perceived stigma and perceived causes of mental illness, and should have a voice in the choice and dosage of medication treatment.

However, while appropriate psychopharmacological treatment is very important in the treatment of serious mental illness, such treatment is rarely sufficient by itself, particularly in the community. While increased emphasis should be placed on attitudes toward medications, this should not occur at the expense of other treatment and psychosocial rehabilitation efforts.

Limitations

Several limitations of this study should be noted, including statistical, measurement, and theoretical. The most significant limitation is the small sample size, which limited the range of potential socio-demographic, clinical, and system-level predictors, and reduced the power needed to detect significant results and to test possible interaction effects of predictor variables. The models that tested combinations of variables (models 2-4 of both aftercare adherence and rehospitalization) over a basic risk model were most seriously affected by power limitations (with power ranging between

.41 and .55). The predictor variable to case observations ratio ranges from 1 predictor to

12 observations (1:12 in risk models), to 1 predictor to 5 observations in the full models

(1:5 in model 4 aftercare). The small sample size clearly limited the possibility to find significant results when testing the combined models.

Sampling adequacy was also a concern for a number of the socio-demographic, clinical, and system variables. Variables had to be collapsed across a number of dimensions, resulting in loss of information and theoretically meaningful categorizations.

100

Some variables that had been found to be predictive in past research could not be tested because of their low rate of occurrence at the research site (for example, only 6.7% of the sample were involuntarily admitted to the index admission), or due to large number of missing data points for some of the risk variables.

Some of the Likert-scale instruments utilized to approximate the constructs of the

HBM and TTM showed scale attenuation effects. For example, the internalized subscale of the TMQ evidenced a larger amount of scale scores clustered toward high levels of expressed internalized motivation. While the assumptions of logistic regression do not include normality or homogeneity of variance, scale attenuation might suggest that the scale/instrument does not properly measure the construct it purports to measure (validity), or that the range of a variable is greater than the range of the measurement scale.

One of the most obvious measurement concerns might be response bias. A classic study by Gove and Geerken (1977) indicated that while response bias is an issue in survey research, the different types of response biases investigated in their study did not have as strong or systematic an effect on the relationships between a number of independent variables and dependent variables (several measures of mental health) as often feared. However, response bias cannot be ruled out in the current study, as it was not independently assessed. It is possible that participants generally wanted to present themselves in a favorable light given their impending discharge, although confidentiality of their answers on the questionnaires to hospital staff had been assured.

Conceptualizing both outcome variables as dichotomies also limits the possibly complex relationship that might underlie service adherence and/or rehospitalization. For example, awareness of illness might be inversely related to increasing total numbers of

101 rehospitalizations, and not be an appropriate construct to measure in the context of comparing one to no rehospitalizations, as one rehospitalization may to due to many issues, such as being discharged prematurely and experiencing a medication reaction.

This type of explanation would become less plausible if an individual is repeatedly rehospitalized over a longer outcome period, and a construct such as insight might explain some aspect of this phenomenon. Conceptualizing aftercare adherence as a dichotomy is still theoretically meaningful if it is conceptualized as an indicator to possible, successful linkage. For example, attending an initial appointment may engage the patients, which in turn, may lead to continued outpatient services. Different variables and processes might be predictive of initial contact versus intermittent versus sustained aftercare.

A final measurement limitation of the present study concerns the method by which participants were asked to complete the URICA. All participants were asked about their primary problem (mental health, substance abuse, or both) and to complete the

URICA based on this problem. The results might have been different had they been instructed to complete a URICA on each problem separately. Finally, a single measure of aftercare adherence was used (as compared to self-report plus collateral information), and longer term and more diverse measures of adherence might have yielded different results.

The data available prohibited an analysis of the relationship between aftercare adherence and rehospitalization. Secondary service utilization data are more limited with regard to aftercare utilization than with regard to rehospitalization. First, some types of service delivery (such as peer support service or clubhouse attendance) are not captured in such data, and the available data made it difficult to ascertain how routine medication

102 screens with a community mental health center psychiatrist were billed, or if an individual went to a walk-in clinic. Second, behavioral health service utilization data do not reflect whether some individuals received their aftercare services from general medical sources such as a primary care physician or one of the community health clinics, rather than a community mental health center.

The most important theoretical limitation of the current study resulted from the inability to explore the relationship between aftercare attendances and rehospitalization.

This constrained the theoretical model of rehospitalization and needs to be included as an important variable in the prediction of rehospitalization following an index hospital discharge. The limitations in adequately capturing the components of the Health Belief

Model have already been outlined in the previous discussion of the results. Cues to action, one of the components of this model, could not be assessed in the current research, but should be included in future studies of the HBM in relation to treatment adherence.

A final theoretical limitation lies in the attempt to assess attitudes to predict behavior. Several problems with the prediction of behavioral intentions versus actual behavior have already been explored. While measuring consumers’ attitudes/perceptions toward their illness and/or services should continue, certain possible mediator and moderator variables, such as length of time between measurement and behaviors of interest, need to be measured and evaluated.

Directions for Future Research

The current study confirmed that static risk factors, while important, are not sufficiently predictive of aftercare adherence or hospital recidivism. These risk factors

103 are often highly sample-specific, but ongoing research has been converging on a small but increasingly reliable number of risk factors that are related to the outcomes in question. Most important are system factors, such a continuity of care (time between discharge and aftercare appointment, having an established outpatient provider), and/or providing linkage strategies between inpatient and outpatient settings as demonstrated by

Boyer et al (2000). Some clinical factors have also been shown to be predictive, for example, involuntary admission status (Boyer et al., 2000; Compton et al., 2006) that has been conceptualized as a proxy variable for insight. Future research needs to continue to explore these variables and tighten their operationalization to identify a generalizable set of empirical static risk variables for this type of prediction research. In light of the findings of the current study, and despite their inherent limitations, is possible that this approach currently hold the best promise in order to predict aftercare adherence, rather than investigating more complex cognitively mediated variables. This approach is favored by those researchers who are interested in arriving at a set of easily available risk factors that have utility in the prediction of the behavior in question, and which in turn might inform clinicians’ decision making about (for example) discharge readiness of an individual.

However, the alternative viewpoint suggests that research into theoretically derived models of health care decision making should also continue to develop broader frameworks for behavioral health care decision making. These models can guide research through hypothesis testing and direct the provision of health care interventions.

The results of the current study inform the direction of future research in several ways.

Dynamic variables remain compelling in prediction models, as they are not only

104 theoretically important, but potentially amenable to therapeutic interventions (e.g., attitudes toward psychiatric medication, motivational levels, etc.). In the domain of prediction research of future violence toward others, the HCR-20, Version 2 (Historical,

Clinical, Risk-20) is such an example (Webster, Douglas, Eaves, & Hart, 1997). This instrument is conceptualized as a structured professional judgment, and arrives at violence risk by combining historical (static-documented variables), clinical (dynamic- observed variables), and risk management (speculative-projected variables) domains.

Similarly dynamic variables, such as motivational constructs, remain intuitively compelling for the study of behavior change or behavioral activation, although the current study suggests that these constructs might be insufficiently stable to predict behavior over time. The assumption that motivational readiness and/or internal motivation fluctuates too widely or too rapidly to be useful in the prediction of health care decision making should be assessed in future research by using a different data collection methodology. Discharged study participants should be followed at regular intervals to determine if motivational constructs fluctuate over time, and to examine which contextual variables affect these fluctuations. This might allow a better understanding for the role of motivation in relation to aftercare adherence and rehospitalization. Conversely, motivation might remain an important predictive construct over shorter time intervals, either alone or in conjunction with other mediator or moderator variables, such as the presence or absence of a social support network.

Another important reason for a change in methodology is that in-person follow-up interviews after discharge would allow more accurate measurement of aftercare services received (type and frequency). This type of data collection might also be more sensitive

105 to observing whether help-seeking occurs in other settings such as medical settings, consumer run programs, or non-traditional settings like pastoral counseling. Finally, ongoing longitudinal outcome research might also be more conducive to the examination of other theoretical models that are more dynamic/social in nature, such as the Network

Episode Model (NME). This model, first proposed by Pescosolido and Boyer (1999),

conceptualizes service utilization as a complex series of behaviors resulting from a

combination of individual, contextual, social network, and service system factors.

Empirical testing of models like the NME might provide a better understanding of

contextual and social dynamic variables that could be incorporated into a prediction

model of health care decision making. Such an investigation such utilize a multi-method

(for example interviews with consumers, and family members following the individuals

discharge), and multi-trait approach (for example, measuring attitudes towards

medications but also measuring the actual behavior such as asking family members or

consumer if the medication was taken as prescribed). It is also important to continue to

evaluate and test constructs that provide new paradigms in service delivery such as

consumer empowerment, consumers perspectives of the efficacy of services received in

the past, and service delivery guided by the principles of recovery.

The current research indicated that the HBM might only have limited utility in

constructing a broader framework in which to understand individuals’ health care decision making. However, while failing to predict aftercare adherence in the current study, some of the components of the HBM showed promise in understanding hospital recidivism, and in suggesting interventions that might reduce rehospitalizations of discharged civil patients. The HBM’s limitations in the current study are thought to

106 partially reflect methodological problems that should be rectified in future research to better assess the utility of this model in understanding aftercare adherence and hospital recidivism.

As this model had never previously been utilized to examine aftercare adherence, future research should focus on improving the variables utilized to represent the constructs of the HBM. For example, researchers should add a separate severity of illness perception measure, and use a measure of insight that improves the measurement of this multidimensional construct, as outlined by Goldberg et al. (2001). Using multi- method assessment strategies may also improve the validity of the constructs measured, and add to the breadth of knowledge about a trait or construct. Finally, cues to action, also part of the HBM, should be included in future research, as their potential impact could not be investigated with the current research methodology. These improvements, in addition to increases in sample size, are thought to provide useful information of the future utility of the HBM as a useful theoretical framework for understanding and predicting aftercare adherence.

Even if such methodological improvements are made, it is unlikely that the HBM alone is an adequate model to predict aftercare adherence or hospital recidivism. Its main limitation arises from conceptualizing human decision making as a “rational” process of weighing costs and benefits of a given decision. While the variables of the HBM can be conceptualized as “dynamic,” the model is limited in accounting for cultural factors, and the role of social networks. Future research might therefore ultimately look at which components of motivational models, the HBM, and social network models, might interact for certain groups of consumers to better understand and predict consumers’ aftercare

107 decision making. These models need to be adequately tested and validated before variables derived from them could be utilized in the prediction of aftercare services.

Findings should be utilized to improve upon existing interventions, and increase providers’ and consumers’ knowledge about which interventions are important for which populations to build improved models of continuity of care. Simultaneously, the development and evaluation of novel service delivery systems should continued, as these improved service systems might play a vital role in improving treatment adherence and in reducing rehospitalizaiton. Such service systems need to improve the continuity of care by reducing the fragmentation of mental health care service provision, focus more on psychiatric rehabilititation (including employment, living situations, etc.) and other outcomes of importance to consumers, illness management, and peer supports.

Conclusions

Research into correlates of continuity of care and rehospitalization remains an important endeavor to improve the functioning of individuals with serious mental illness, and/or co-occurring disorders. The purpose of the current study was to utilize a theory- driven approach to examine predictors of aftercare adherence and rehospitalization. The prediction of aftercare attendance by assessing study participants’ health beliefs or motivation was not accurate. These finding were disappointing in light of their potential implications for clinical service delivery during acute inpatient hospitalizations. The prediction of hospital recidivism was however significantly improved over a basic

“static” risk factor model, by assessing study participants’ attitudes that were chosen to approximate the Health Belief Model. More specifically, participants’ negative attitudes toward psychiatric medications significantly increased the likelihood of rehospitalization,

108 an important finding with clinical implications during a patient’s inpatient stay. Attitudes towards psychiatric medications can be modified as shown by Kemp et al. (1996, 1998) who used “compliance therapy,” a therapy approach that combines a mixture of motivational interviewing and psychoeducational groups.

Finally, some of the methodological and statistical problems identified in the current study constrained the testing of a more comprehensive model of aftercare adherence and rehospitalization as proposed by Klinkenberg and Calsyn (1996) which includes client vulnerability, community support, system responsiveness, and receipt of aftercare. Receipt of aftercare was not regressed on hospital recidivism, as the nature of the secondary data did not allow a determination of whether a rehospitalization followed or preceded a participant’s aftercare service contact.

The prediction of aftercare adherence and rehospitalization is most likely multiply determined by static, dynamic, social, and cultural factors. The most common approach to the prediction of aftercare and rehospitalization has been to find practical and readily accessible predictors of aftercare adherence. The most reliable predictors identified to date have been system variables, and a small number of clinical variables. The significance of system variables, for example, in providing better linkages between inpatient and outpatient settings, cannot be overemphasized. However, on a policy and programmatic level many service systems have been slow in responding to these findings. Additionally, persistent mental illness has been identified as an additional risk factor of keeping appointments, even in the presence of certain linkage strategies. The current study showed that “dynamic” variables, such as attitudes toward psychiatric medications, can play an important role in the prediction of rehospitalization. Dynamic

109 variables are of special interest to clinicians as they can be subject to clinical interventions during a hospitalization. Therefore, the identification of reliably predictive dynamic variables, in addition to reliable clinical and other more traditionally “static” variables, continues to be an important area of research. In addition the development and evaluation of novel service delivery systems that are more responsive to consumer needs is an equally important endeavor in order to improve aftercare adherence and in turn reduce acute illness relapse and rehospitalizations.

Therefore, it is believed that with methodological improvements (for example, a longitudinal study design with in-person follow-up interviews after discharge to reliably measure aftercare treatment adherence) and a larger sample size, theoretically derived dynamic variables (including social support networks and past experiences, and other variables modeled on newer models such as the NEM) in addition to static variables, continue to hold promise in explaining and subsequently predicting consumers’ health care decision making.

110

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Appendix A: The Health Belief Model

INDIVIDUAL MODIFYING LIKELIHOOD OF PERCEPTIONS FACTORS ACTION

Age, Sex, Ethnicity Perceived benefits Personality versus Socio-economics barriers to Knowledge behavioral change

Perceived susceptibility/ Perceived threat of Likelihood of seriousness of disease disease behavioral change

Cues to action -education -symptoms -media information

Source: Glanz et al., 2002, p. 52

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Appendix B: Participant Recruitment Flow Sheet

Admission of all patients:

(n=561)

Not Eligible: Eligible: (n=354) (n=197)

Reason: Discharged before Approached: • Age (n=33) approach: • < 48 hr stay (n=60) (n=94) (n=103) • Not fluent English (n=8) • Not Phila. resident (n=20) • Medicare/Private Insurance (n=53) • Discharged to another Refused: Interviewed:

inpatient facility (n=18) (n=19) (n=84) • > 45 day stay (n=7) • MR-or Head injury (n=27) • End stage medical illness (n=4) • No previous outpt Tx past 2 Subsequently excluded: yrs (n=44)

• Left AMA (n=10) (n=10) • Readmitted during study period (n=21) • Multiple reasons above (n=44) • Other (n=5)

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Appendix C: Copies of Measures

AII

Instructions: Let's begin by talking a bit about how you came to be in the hospital and some questions about how you view your problems with your emotions, nerves, or mental health. I am going to ask you a few questions and will be making some notes about what you are telling me. You can see what I am writing down if you'd like:

1. What led to you coming to the hospital? (by self/ by others/because of what symptoms)

2. Do you have a mental illness or symptoms of a mental illness?

3. What is the most bothersome emotional or psychiatric symptom you have and what causes this symptom?

4. Do you feel in need of psychiatric treatment [or treatment for your emotional problems]?

5. Does medication help your (emotional and psychiatric) symptoms and problems?

6. Do you feel that if you receive outpatient psychiatric, or dual diagnosis treatment, after you leave the hospital that it will be helpful to you?

7. How likely is it that you will seek psychiatric treatment as an outpatient?

[ask Scale 1-10 where 10=most likely]

1 2 3 4 5 6 7 8 9 10

133 DAI-10

Next I am going to read you some statements about psychiatric medications. The aim of this questionnaire is to gain some understanding of what people think about medications and what experiences people have of them. There are no right or wrong answers. Please give YOUR OWN OPINION, not what you think we might want to hear.

I please tell me whether each statement is true as applied to you or false as applied to you. The medications referred to are those for mental health needs only.

[If a statement is not worded quite the way you would put it, please decide whether the answer is mostly true or mostly false to you. Do not spend too much time on any one question.]

1. For me, the good things about medication outweigh the bad T F

2. I feel strange, “doped up,” on medication T F

3. I take medications of my own free choice T F

4. Medications make me feel more relaxed T F

5. Medication make me feel tired and sluggish T F

6. I take medication only when I feel ill T F

7. I feel more normal on medication T F

8. It is unnatural for my mind and body to be controlled by medications T F

9. My thoughts are clearer on medication T F

10. Taking medication will prevent me from having a breakdown T F

134 MHCS

Instructions: We would now like to know how confident you are about your ability to help yourself deal with those things that commonly influence our lives. I am going to read you some statements and for each item, indicate HOW CONFIDENT you are that you could be doing something to help yourself after you leave the hospital. Please rate the degree of your confidence by choosing a number of from 1 to 6, where 1= very non-confident and 6=very confident.

Very non-confident = 1 Non-confident = 2 Somewhat non-confident = 3 Somewhat confident = 4 Confident = 5 Very confident = 6

Let's begin: How confident are you that after your discharge from here you will: [interviewer re-read intro sentence every 3-4 questions]

1. Be happy

1 2 3 4 5 6

2. Feel hopeful about the future

1 2 3 4 5 6

3. Set goals for yourself

1 2 3 4 5 6

4. Get support when you need it

1 2 3 4 5 6

5. Make friends

1 2 3 4 5 6

6. Stay out of the hospital

1 2 3 4 5 6

135 7. Be able to face a bad day

1 2 3 4 5 6

8. Deal with losing someone close to you

1 2 3 4 5 6

9. Deal with feeling depressed

1 2 3 4 5 6

10. Deal with feeling lonely

1 2 3 4 5 6

11. Deal with nervous feelings

1 2 3 4 5 6

12. Deal with symptoms related to your mental illness diagnosis

1 2 3 4 5 6

13. Say no to a person abusing you

1 2 3 4 5 6

14. Use your right to accept or reject mental health treatment

1 2 3 4 5 6

15. Advocate for your needs

1 2 3 4 5 6

136

CABS

Score: Unless otherwise indicated score 1=never 2=sometimes 3=usually 4=always

Next we are going to ask your opinions about your past outpatient services (not self help). Who was your last outpatient provider and what did you do there (if more than one write them down, and focus on mental health)?

When did you last attend services there (such as case management, psychiatrist, therapist, partial program, etc.)? (interviewer: get at least approximate date i.e. last week, last month, more than 6 months ago, etc.):

______

Okay, let’s get started. With regard to [insert name of provider above]

A. Evaluation of Behavioral Health Services

1=never 2=sometimes 3=usually 4=always

1. Do you have access to help 1 2 3 4 when needed in a crisis or emergency?

2. Do you have access to help 1 2 3 4 when needed during the week?

3. Do you have access to help 1 2 3 4 when needed on evenings or weekends?

4. Are you able to obtain an 1 2 3 4 appointment in a timely manner?

5. Do you wait more than 20 1 2 3 4 minutes past your appointment time?

137 B. Continuity of Care

6. Is there one clinician that provides most of the care you receive?

YES NO

1=never 2=sometimes 3=usually 4=always 7. How often do you see a different 1 2 3 4 clinician from the one you want?

8. Do clinicians know 1 2 3 4 what they should do about your mental health history?

C. Provision of Information

9. Do clinicians tell you about benefits and risks of medication?

YES NO

1=never 2=sometimes 3=usually 4=always

10. Do clinicians tell you what 1 2 3 4 to do if you have side effects from medication?

11. Do clinicians give you 1 2 3 4 information or advice about how to maintain mental health?

12. Do clinicians tell you about the different kinds of treatment available?

YES NO

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D. Interaction 1=never 2=sometimes 3=usually 4=always

13. Are you treated with 1 2 3 4 courtesy and respect by the office staff?

14. Do clinicians listen 1 2 3 4 carefully to you?

15. Do clinicians explain 1 2 3 4 things in a way you can understand?

16. Do clinicians show 1 2 3 4 respect for what you have to say?

17. Do clinicians spend 1 2 3 4 enough time with you?

18. Do clinicians give 1 2 3 4 you reassurance and support?

19. Do you agree with 1 2 3 4 clinicians about how to deal with your problems?

20. Are you involved in 1 2 3 4 decisions about your treatment?

21. Do clinicians involve your family in treatment as much as you want?

YES NO

139

E. Rights and Confidentiality

22. Do clinicians tell you that you have the right to refuse treatment?

YES NO

23. Do clinicians give out confidential information about you?

YES NO

24. Is the office staff helpful? 1=never 2=sometimes 3=usually 4=always

25. How much were 1=not at 2=somewhat 3=quite a 4=a great you helped by all bit deal outpatient treatment?

F. Global evaluation

26. Would you recommend your clinician to someone else?

YES NO

27. What is your overall rating of the outpatient mental or dual diagnosis health care you received?

(1= worse possible ------10 Best possible)

1 2 3 4 5 6 7 8 9 10

140

TMQ

Instructions: The next questionnaire concerns people's reasons for entering treatment and their feelings about treatment. Different people have different reasons for entering treatment, and we want to know how true each of these reasons are for you. Please indicate how true each reason is for you, using the following scale.

Please know that when a question mentions “treatment”, or “the program,” it means the outpatient treatment program that you are being referred to when you leave here, not the treatment you have received here at the PMCU:

1 2 3 4 5 Very much or Not at all true Slightly true Somewhat true Mostly true completely true

A. I will be going for treatment at the clinic because:

1. I really want to make some 1 2 3 4 5 changes in my life.

2. I won't feel good about myself if I 1 2 3 4 5 don't get some (some more) help.

3. I was referred by the legal system. 1 2 3 4 5

4. I feel so guilty about my problem 1 2 3 4 5 that I have to do something about it.

5. It is important to me personally to 1 2 3 4 5 solve my problems.

B. If I remain in treatment it will probably be because:

6. I'll get in trouble if I don't. 1 2 3 4 5

7. I'll feel very bad about myself if I 1 2 3 4 5 don't.

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8. I'll feel like a failure if I don't. 1 2 3 4 5

9. I feel like it's the best way to help 1 2 3 4 5 myself.

10. I don't really feel like I have a 1 2 3 4 5 choice about staying in treatment.

11. I feel it is in my best interest to 1 2 3 4 5 complete treatment.

C. Rate each of the following in terms of how true each statement is for you.

12. I will go to treatment because 1 2 3 4 5 I will be under pressure to go.

13. I am not sure the program will 1 2 3 4 5 work for me.

14. I am confident the program 1 2 3 4 5 will work for me.

15. I decided to go to treatment 1 2 3 4 5 because I am interested in getting help (ongoing help). 16. I'm not convinced that the 1 2 3 4 5 program will help me with my mental health problems/ and or substance abuse. 17. I want to openly relate with 1 2 3 4 5 others in the program.

18. I want to share some of my 1 2 3 4 5 concerns and feelings with others.

19. It will be important for me to 1 2 3 4 5 work closely with others in solving my problem.

142 20. I am responsible for the 1 2 3 4 5 upcoming choice of treatment.

21. I doubt that the program will 1 2 3 4 5 solve my problems.

Rate each of the following in terms of how true each statement is for you.

22. I look forward to relating to 1 2 3 4 5 others who have similar problems.

23. I chose the outpatient 1 2 3 4 5 treatment because I think it is an opportunity for change.

24. I am not very confident that I 1 2 3 4 5 will get results from outpatient treatment this time.

25. It will be a relief for me to 1 2 3 4 5 share my concerns with other outpatient program participants.

26. I accept the fact that I need 1 2 3 4 5 some help and support from others to beat my problem.

143 MHSIP Version 1.1

Instructions: In order to provide the best possible mental health services, we need to know what you think about the outpatient services you received during the last year, the people who provided it, and the results. There is space at the end of the survey to comment on any of your answers. Please indicate your agreement/ disagreement with each of the following statements by circling the number that best represents your opinion. If the question is about something you have not experienced, circle the number 9 to indicate that this item is "not applicable" to you.

Okay, let’s get started. With regard to [insert name of provider given in CABS]

[If no outpatient services within 1 year indicate here:] ______

[Were they referred at some point? If yes, why did they not go?] ______

1. I like the services that I received there.