COSTS, BENEFITS, AND COST-BENEFIT OF GENDER-SENSITIVE INTENSIVE INPATIENT TREATMENT FOR PARENTING AND NONPARENTING SUBSTANCE ABUSERS. Sarah E.

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1 COSTS, BENEFITS, AND COST-BENEFIT OF GENDER-SENSITIVE INTENSIVE INPATIENT TREATMENT FOR PARENTING AND NONPARENTING SUBSTANCE ABUSERS By Sarah E. Hornack Submitted to the Faculty of the College of Arts and Sciences of American University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy In Clinical Psychology Chair: 2014 American University Washington, DC 20016

2 COPYRIGHT by Sarah E. Hornack 2014 ALL RIGHTS RESERVED

3 The source of my joy and strength is my husband, Chris. My work is a representation of the encouragement he shares with me every day.

4 COSTS, BENEFITS, AND COST-BENEFIT OF GENDER-SENSITIVE INTENSIVE INPATIENT TREATMENT FOR PARENTING AND NONPARENTING SUBSTANCE ABUSERS BY Sarah E. Hornack ABSTRACT Gender-sensitive (GS) substance abuse treatment aims to provide services tailored to women s needs. Some services, such as assessment of parenting skills, focus on women s role as mothers and the familial impact of substance abuse. The current investigation examines the service utilization (healthcare, income assistance, criminal justice involvement) and earned income of 14,497 individuals who received substance abuse treatment at one of 13 mixedgender, intensive inpatient programs (IIPs) varying in the level of gender-sensitive treatment provided. The cost of gender-sensitive treatment was calculated for each patient and was compared to service utilization and earned income two years preceding and following IIP treatment. It was expected that patients who were female, had children, and were receiving higher levels of gender-sensitive treatment would demonstrate greater reductions in service utilization and greater increases in earned income, offsetting the cost of treatment. Calculated benefits showed that service utilization mostly increased and earned income decreased after treatment, resulting in largely negative values. Analyses showed significant differences in treatment cost, benefits, and net benefits across levels of gender-sensitive treatment, generally demonstrating that the lowest level of gender-sensitive treatment was more costly and produced lower benefit values. Hierarchical regressions revealed significant relationships between key variables and specific benefits and net benefits, but not total net benefit. Although not ii

5 completely aligned with project expectations, the results suggested that female gender, being a parent, and higher gender sensitivity variably impacted the costs and benefits of substance abuse treatment and remain important factors to consider in this line of research. iii

6 ACKNOWLEDGMENTS First and foremost, I would like to thank my advisor, Dr. Brian Yates, for his guidance in all things research and program evaluation-related, and also for being a great human being. My Program Evaluation Research Lab colleagues, Katheryn Ryan, Jay Gorman, Lana Wald, and Alexis French have provided much support throughout my grad school career. I am also very appreciative of the team at Westat, Dr. Wendy Kissin, Dr. Robert Orwin, Dr. Ronald Claus, Dr. Zhiqun Tang, and Dr. Carlos Arieira for their advice and contributions. Last, but not least, I would like to thank my committee, Dr. Laura Juliano, Dr. GiShawn Mance, and Dr. Cora Lee Wetherington, for their input and ideas. iv

7 TABLE OF CONTENTS ABSTRACT... ii ACKNOWLEDGMENTS... iv LIST OF TABLES...viii LIST OF ILLUSTRATIONS..x CHAPTER 1 COSTS, BENEFIT, AND COST-BENEFIT OF GENDER-SENSITIVE INTENSIVE INPATIENT TREATMENT FOR PARENTING AND NONPARENTING SUBSTANCE ABUSERS Societal Effects of Drug Abuse..1 Benefits of Substance Abuse Treatment 2 Healthcare Utilization.2 Employment and Independent Income...3 Criminal Justice Involvement.3 Gender Differences in Substance Abuse and Treatment Trajectories...4 Defining Gender-Sensitive Treatment...5 Outcomes of Gender-Sensitive Treatment.5 Cost of Gender-Sensitive Treatment..6 Cost-Benefit of Gender-Sensitive Treatment.6 Project Expectations...7 CHAPTER 2 METHOD Participants.9 Programs...11 Measures..11 v

8 Databases..12 Procedure..13 Cost Assessment of Index Treatment 13 Assessing the Monetary Value of Outcomes.13 Cost-Benefit Analysis...15 CHAPTER 3 RESULTS 18 Analysis of Possible Covariates 18 Analyzing the Cost of Gender-Sensitive IIP Treatment...19 Benefits of Gender-Sensitive Treatment..20 Multivariate Analyses of Benefits.21 Net Benefits of Gender-Sensitive Treatment 23 Multivariate Analyses of Net Benefits...24 Total Net Benefit of Gender-Sensitive Treatment 25 Multivariate Analyses of Total Net Benefit...26 CHAPTER 4 DISCUSSION..28 Does Gender-Sensitive Treatment Cost More?...28 Are Specific Benefits and Net Benefits of Gender-Sensitive Treatment Greater?...29 Is the Total Net Benefit of Gender-Sensitive Treatment Greater?...31 Project Limitations 32 Future Research into the Cost of Gender-Sensitive Treatment 34 Conclusion 35 APPENDIX A FIGURE DEMONSTRATING POTENTIAL TRAJECTORY FOR HEALTH CARE SERVICE USEAGE..37 vi

9 APPENDIX B ILLUSTRATION OF HYPOTHESIZED BENEFITS AND COST-BENEFIT...38 APPENDIX C MEASURES UTILIZED IN DEVELOPING THE GENDER-SENSITIVE SCALE...39 APPENDIX D CALCULATION OF CRIMINAL JUSTICE COSTS.41 APPENDIX E GRAPHICAL ANALYSES OF BENEFITS ACROSS THE STUDY PERIOD..43 APPENDIX F CHARACTERISTICS OF PARTICIPANTS WHO DIES DURING FOLLOW- UP PERIOD...52 APPENDIX G DESCRIPTION OF MEAN BENEFITS ACROSS LEVEL OF GENDER- SENSITIVE TREATMENT, GENDER, AND PARENTING STATUS.53 APPENDIX H DESCRIPTIVE INFORMATION FOR AVERAGE TOTAL BENEFITS ACROSS LEVEL OF GENDER-SENSITIVE TREATMENT 55 APPENDIX I DESCRIPTION OF MEAN NET BENEFITS ACROSS LEVEL OF GENDER- SENSITIVE TREATMENT, GENDER, AND PARENTING STATUS.56 APPENDIX J EXPLORATORY ANALYSES OF SPECIFIC NET BENEFITS 58 APPENDIX K DESCRIPTIVE AND EXPLORATORY ANALYSES OF TOTAL NET BENEFIT...59 APPENDIX L IMPACT OF THE RECESSION ON TOTAL NET BENEFIT...61 APPENDIX M FACTORIAL ANOVA EXAMINING RELATIONSHIP OF GENDER, PARENTING STATUS, AND GENDER-SENSITIVE TREATMENT ON TOTAL NET BENEFIT...62 APPENDIX N DETAILS OF REGRESSION MODEL PREDICTING TOTAL NET BENEFIT...63 REFERENCES vii

10 LIST OF TABLES Table 1. Demographic Characteristics of Participants Number of Participants Reporting Parenting Status, by Gender Service Utilization Datasets Mean Length of Stay (LOS) and Cost of Index Intensive Inpatient Program, by Level of Gender Sensitivity Mean Net Benefit For All Patients Across Service Utilization and Earned Income Domains, n = 14, Crime-Specific Societal Cost Estimates Frequency of Deceased Participants Across Levels of Gender-Sensitive Treatment Average Benefit Values for Healthcare Utilization Average Benefit Values for Economic Assistance Average Benefit Values for Earnings Average Benefit Values for Criminal Justice Services Average Total Benefit Values by Level of Gender-Sensitive Treatment Average Net Benefit Values for Healthcare Utilization Average Net Benefit Values for Economic Assistance Average Net Benefit Values for Earned Income Average Net Benefit Values for Criminal Justice Services Results from Kruskal-Wallis ANOVA Tests for Net Benefits by Gender-Sensitive Treatment Level Average Total Net Benefit by Gender-Sensitive Treatment Level Summary of Simple Two-Way Interaction Findings for Total Net Benefit..62 viii

11 20. Summary of Hierarchical Regression Model (Step 4) to Predict Total Net Benefit..63 ix

12 LIST OF ILLUSTRATIONS Figure 1. Stacked Bar Graph Displaying Totals Across All Individuals, Each Month with Benefit Domains Labeled Bar Graph Demonstrating the Mean, Total Net Benefit Across Levels of Gender-Sensitive Treatment, with Confidence Interval Bars An Illustration of Potential Offset Health Care Savings Resulting from Substance Abuse Treatment (Holder, 1998) Hypothesized Value of Benefits for Each Level of GS Treatment and Participant Category, Larger Dollar Signs Indicating Greater Benefit as a Result of Gender-Sensitive Treatment Line Graphs Representing Mean Economic Assistance, Healthcare Utilization, Earned Income, and Criminal Justice Involvement by Participants in Each Level of Gender-Sensitive Treatment Across the Study Period Line Graph Representing the Average Medicaid Reimbursement of All Participants for Healthcare Services Each Month of the Study Period Line Graph Representing Average Medicaid Reimbursement for Healthcare Services by Gender of Participant Line Graph Representing Average Medicaid Reimbursement for Healthcare Services by Parenting Status of the Participant Line Graph Representing Average Medicaid Reimbursement for Healthcare Services by Parenting Status of the Participant Line Graph Representing the Average Economic Assistance by Gender Over the Study Period Line Graph Representing the Average Economic Assistance by Parenting Status Line Graph Representing the Average Earned Income Received by All Participants Each Month of the Study Period Line Graph Representing the Average Earnings Received by Men and Women Across the Study Period Line Graph Representing the Average Earnings by Parenting Status Across the Study Period 49 x

13 15. Line Graph Representing the Average Criminal Justice Cost of All Participants Each Month of the Study Period Line Graph Representing the Average Criminal Justice Cost by Gender for Each Month of the Study Period Line Graph Representing the Average Criminal Justice Cost of By Parenting Status Each Month of the Study Period Mean Net Benefit for Parents and Nonparents by Gender across Levels of Gender-Sensitive Treatment Bar Graph Representing the Mean, Total Net Benefit Across Treatment Cohorts, with Confidence Interval Error Bars Bar Graphs to Explore the Impact of Gender, Parenting Status, and Gender-Sensitive Treatment on Overall Net Benefit, with Confidence Interval Bars..62 xi

14 CHAPTER 1 COSTS, BENEFITS, AND COST-BENEFIT OF GENDER-SENSITIVE INTENSIVE INPATIENT TREATMENT FOR PARENTING AND NONPARENTING SUBSTANCE ABUSERS The effects of drug dependence extend from the affected individual to their family and all of society. Family functioning is disrupted when parents who regularly use substances are less present in the household or even no longer financially contributing to the family. Alarmingly, parental substance abuse also can potentiate child maltreatment and consequential reporting to child protective services (Wolock & Magura, 1996). Further studies have shown that the substance abuse of a parent can have negative long-term effects in the form of intergenerational drug use, as their children are more susceptible to developing a substance use disorder (Merikangas, Rounsaville & Prusoff, 1992). Societal Effects of Drug Abuse Drug and alcohol abuse also impact communities and society at large. In particular, substance abuse has been shown to increase societal costs related to crime and criminal justice, infectious diseases, hospitalizations and emergency room visits, co-occurring mental health issues, and social programs, i.e. welfare services (Ettner et al., 2006). Further evidence indicates that substance abuse leads to reduced on-the-job productivity or loss of employment altogether, creating a greater burden on society should individuals then require income assistance (Single et al., 2001). When substance abuse treatment is successful, it is possible that some of these costs to society may be mitigated. 1

15 Benefits of Substance Abuse Treatment If these substance abuse-related costs to society are prevented, it has been shown that they can outweigh the cost, several times over, of substance abuse treatment itself (Cartwright, 2000; Holder, 1998). Using data from California, New York and Washington states, substance abuse treatment has resulted in benefits, specifically the monetary value of increased employer earnings and services not used because successful treatment negated their need (U.S. Department of Health and Human Services, 2009). One study comparing the cost of treatment to societal monetary benefits found a cost-benefit ratio of 1:7 (Ettner et al., 2006). Several categories of potential benefits stemming from substance abuse treatment are detailed below. Healthcare Utilization Due to associated medical issues that often accompany prolonged substance use, reduced healthcare utilization is often noted as a long-term benefit of successful substance abuse treatment. In a review of cost-benefit studies, Holder (1998) found that once substance abuse treatment was initiated, health care utilization and costs consistently decreased to a level that was below pretreatment costs over a follow-up period of two to four years. A common trajectory of healthcare utilization is that service usage increases at the time of treatment entry and then decreases over time [see Appendix A]. In addition to chronic health issues that arise with prolonged substance abuse, alcohol use disorders are the leading cause of injury in the U.S. The delivery of outpatient interventions to address alcohol use has been shown to be cost-beneficial, reducing future injury and hospitalization and producing a cost-benefit ratio of 1: 3.81 (Gentilello, Ebel, Wickizer, Salkever, & Rivara, 2005). Another specific benefit of substance abuse treatment may be fewer 2

16 trips to the emergency room posttreatment, being due to the availability of supports that promote use of preventative care and to the reduced likelihood of injuries and emergent issues (Ettner et al., 2006; Gentilello et al., 2005). Employment and Independent Income Benefits may also include licit income earned after successfully completing substance abuse treatment. Koenig et al. (2005) found that employment among individuals participating in substance abuse treatment rose from 21% at baseline to 45% at 24-month follow-up. Among their sample, earnings were observed to rise 2.9% yearly. Also, over the 30-month research period, receipt of welfare and economic assistance was seen to decline. Criminal Justice Involvement Possession and purchase of illegal drugs are crimes in themselves, and drug use has been shown to lead to other forms of crime (Miller, Cohen, & Wiersema, 1996). Furthermore, the costs to taxpayers in funding the criminal justice system are great. In Washington State, the average household was estimated to contribute $1,062 in taxes per year towards the criminal justice system (WSIPP, 2003). Harwood, Hubbard, Collins, and Rachal (1994) demonstrated that residential, methadone outpatient treatment, and drug-free outpatient treatment all yield costbenefit ratios where treatment cost is outweighed by reductions in criminal justice involvement. Data from the California Drug and Alcohol Treatment Assessment (CALDATA) indicated that costs related to criminal activity declined by two-thirds after treatment (Gerstein et al., 1994). 3

17 Gender Differences in Substance Abuse and Treatment Trajectories Men and women present to substance abuse treatment with differences in the progression of their disorder, co-morbid difficulties, and ease of accessing services. In comparing the developmental trajectories of substance dependence among male and female substance users, a telescoping effect has been demonstrated, meaning that the progression of addiction is quicker for women (Brady & Ashley, 2005; Haas & Peters, 2000). Related to this trend, the negative health and psychiatric issues that accompany substance abuse have also been shown to be greater in women (Grella, 2007; Najavits, Rosier, Nolan & Freeman, 2007). Women in substance abuse treatment are more likely to report having a partner who abuses substances, previous domestic violence, domineering intimate partner relationships, and poor self-esteem compared to males in substance abuse treatment (Grella, 2007; Rivaux, Sohn, Armour, & Bell, 2008; Schlesinger, Susman, & Koenigsburg, 1990; Walitzer & Sher, 1996). It has been repeatedly noted that men and women experience different barriers to entering treatment once a substance use problem is identified, including financial obstacles and stigma. For instance, males entering treatment have higher educational attainment, higher rates of employment and private insurance compared to females; therefore, males are in a better socioeconomic position to access treatment (Wechsberg, Craddock, & Hubbard, 1998; Wong, Badger, Sigmon, & Higgins, 2002). Greater levels of social stigma, associated feelings of guilt, and the higher likelihood of being the primary caregiver for dependents all present obstacles to treatment for women (Nelson-Zlupko, Kauffman, & Dore, 1993; Rivaux, Sohn, Armour, & Bell, 4

18 2008). Given these unique issues, it seems intuitive that women and men may require specialized treatment services to best meet their specific needs. Defining Gender-Sensitive Treatment Gender-sensitive treatment is defined, essentially, as treatment tailored to the specific needs of women. This type of treatment typically involves the inclusion of additional services to address women s different needs, such as trauma-focused services, self-efficacy training, relationship group therapy, vocational services, and parent training (Greenfield et al., 2007; Grella & Joshi, 1999). Of programs providing specialized services for women, 41% provided services related to domestic violence, 17% provided services for pregnant or postpartum women, 18% provided childcare, and 9% provided residential beds for clients children. Programs that sought to address the specific needs of women were more likely to provide additional services that are usually needed by women in treatment such as housing assistance, employment counseling, and coordination with social services (Grella, 2007). Outcomes of Gender-Sensitive Treatment Previous findings have indicated that gender-sensitive treatment produces positive effects for women substance abusers. Sun (2006) reviewed 35 studies to pinpoint programmatic factors contributing to successful treatment outcomes for women and determined these were: singleversus mixed-gender programs, treatment intensity, providing childcare, case management, supportive staff and providing individual counseling. A meta-analysis of 33 studies revealed that women-only substance abuse treatment programs consistently had a positive impact on child and maternal well-being or psychological outcomes but findings related to substance use reduction were inconclusive (Orwin, Francisco, & Bernichon, 2001). Significant improvements in levels of 5

19 drug use, social support, severity of depression and self esteem were found by Copeland, Hall, Didicott, and Biggs (1993) six months after receipt of specialist women s service substance abuse treatment compared to a control, traditional mixed-sex service. Although many others have examined the effectiveness of gender-sensitive treatment, substantiating the cost, benefit, and cost-benefit of this focused treatment is a relatively understudied domain. Cost of Gender-Sensitive Treatment The complex needs of women in substance abuse treatment, including co-occurring mental health and medical issues, often necessitate more intensive or a broader range of services that require additional resources (Penn, Brooks, & Worsham, 2002; Reed & Mobray, 1999). Higher costs for gender-sensitive programs have been documented across residential women and children programs (Burgdorf et al., 2004). Others have demonstrated no significant difference in total costs for specialized trauma-informed substance abuse treatment for women or in the marginal costs of gender-sensitive services (Domino, Morrissey, Nadlicki-Patterson, & Sukyng, 2005; Hornack & Yates, 2014). In both studies of trauma-informed treatment cost and marginal gender-sensitive service costs, a large number of services were provided in both the specialized treatment and comparison treatment groups so there was significant overlap in resources used. Also, in both studies, the mean cost of gender-sensitive treatment cost was somewhat higher but specialized treatment did not cost significantly more. Cost-Benefit of Gender-Sensitive Treatment Among the studies that have examined the cost-benefit of gender-sensitive treatment, French, McCollister, Cacciola, Durrell, and Stephens (2002) determined that residential substance abuse treatment geared towards parenting women was cost-beneficial (taking into 6

20 account change in use of medical services, money spent on drugs, criminal activity, and employment), with an average benefit-cost ratio of 3:1. Daley et al. (2000) explored reductions in crime as an outcome of substance abuse programs for pregnant women. These treatment programs varied in modality of service delivery, but all provided services responsive to the needs of pregnant or parenting individuals, such as childcare, on-site nursing staff, case management, and family therapy. Although all substance abuse treatment modalities (e.g., detoxification, methadone, residential, outpatient, residential/outpatient combined) produced reductions in crime and related costs that outweighed the cost of treatment, the greatest cost-benefit was seen for residential therapy. Both of these studies evaluated women-only programs, so the cost-benefit for male patients was not determined. However, previous findings suggest that the benefits of typical substance abuse treatment are similar for men and women (Mannix, 2010; U.S. Department of Health and Human Services, 1997). The project at hand is unique in examining costs and benefits for parenting men and women associated with receiving varying levels of gender-sensitive substance abuse treatment. Project Expectations The present study followed patients who participated in one of 13 intensive inpatient programs (IIPs) over a period of four years. These programs varied by intensity of gendersensitive (GS) treatment provided, as determined by Tang, Claus, Orwin, Kissin, and Arieira (2012), with four levels defined and level one indicating the least gender-sensitive. The term gender-sensitive is analogous to women-sensitive in the literature, indicating that treatment was designed with women s needs in mind. For this reason, a primary expectation was that women would show significantly greater benefits across economic assistance, employment, 7

21 criminal justice involvement and healthcare utilization compared to men in the parent and nonparent groups. It was expected that parents of both genders who received higher levels of gender-sensitive treatment would incur greater benefits from treatment than parents receiving lesser levels of GS treatment. This expectation was due to objectives of gender-sensitive treatment that include an emphasis on assessment of parenting skills and training. When compared to nonparents receiving their respective level of gender-sensitive treatment, parents were expected to display greater benefits. It was anticipated that there would be no significant differences in treatment cost among groups because a standard reimbursement rate was used to calculate these values. As parents were not expected to be more costly to treat, their net benefit would be higher compared to nonparents. Despite the fact that women might need longer treatment due to their greater co-occurring issues and might cost more to treat as a result, their net benefit was projected to be significantly greater than that of men. [The figure in Appendix B depicts these hypotheses visually.] In addressing these hypotheses, the current project was designed to contribute to research area of heightened importance given societal focus on health care reform and cost containment. 8

22 CHAPTER 2 METHOD Participants Using Washington state administrative databases, information was available for 14,947 male and female substance-abusing individuals who entered one of 13 intensive inpatient programs participating in a National Institute on Drug Abuse (NIDA)-funded study between the years of 2005 and This large sample was also examined by parenting status and number of children each individual reported. All individuals were recipients of state Medicaid and their use of public services was available for two years pre- and postindex IIP enrollment. Demographic characteristics of this sample are detailed below in Table 1. Table 1. Demographic Characteristics of Participants n Mean Median SD Age 14, Years of Education 14, n % Race Caucasian 10, African-American 1, Other Native American Multi-Racial Pacific Islander Asian-American Employment Status at Intake Unemployed- Not seeking work 7, Unemployed-Seeking work 3, Not Working- Disabled 1, Employed Full Time Employed Part Time Institutionalized Temporary Employment Underage- Not in workforce Homemaker Retired

23 Not in Workforce Unknown Military Primary Drug of Abuse Alcohol 6, Methamphetamine 3, Cocaine 1, Marijuana 1, Heroin 1, Other Opiates and Synthetics Oxy/Hydrocodone Amphetamines Prescribed Opiate Substitute Hallucinogens Benzodiazepines Other Sedatives/Hypnotics Other Treatment Completion Status Treatment Completion 8, Withdraw Against Program Advice Noncompliance/Rule Violation Inappropriate Admission Withdraw with Program Advice Transferred to a Different Facility Other Table 2 details the number of participants reporting whether they had children in the household, by gender. This particular variable had a greater incidence of missing values compared to others (which were typically complete), with only 11,561 individuals reporting number of children. Of those reporting whether they had children, males reported a median of zero children and females reported a median of one child. Table 2. Number of Participants Reporting Parenting Status, by Gender Gender of Parent Parenting Status Male Female Total Parent 3,150 2,597 5,747 Nonparent 4,187 1,627 5,814 10

24 Programs On-site visits were conducted to gather information from substance abuse treatment programs. Initially, 16 mixed-gender, intensive inpatient programs were identified as eligible for the study. Program funding cuts, as well as the realization that two programs were highly segregated and essentially providing women-only treatment, led to the recruitment of 13 programs. From these, Program Directors, Clinical Directors, and counselors were interviewed. Each participating program received a $750 incentive for participation. Program directors also participated in a follow-up phone interview one year after the initial site visit to provide information about changes in costs. Each program was compensated with $100 for participation in the follow-up interview. Due to declining state budgets, two of the original 13 programs had closed in the interim year, so only 11 of the program directors were interviewed at follow-up. All who participated gave informed consent, and American Psychological Association ethical guidelines for research were followed. This involved the approval of Institutional Review Boards for each research institution participating, including the state providing the data. Measures Information from these site visits, specifically from the program director, clinical director, counselor, and patient interviews, were used in learning more about program characteristics. Both interviewers completed an Observation Protocol to describe their views of the program. In general, these measures asked for details about specific services provided, staff salaries and program budget, and the facility/program environment. Items from these measures were then used to create a scale for gender sensitivity (Tang et al., 2012). Additional 11

25 information about the measures and the items used to define the four levels of gender-sensitive treatment can be found in Appendix C. Databases Several state databases provided a wealth of information concerning the needs and service usage of the individuals in the study, pre- and post-iip participation. The table below details the database information available. Table 3. Service Utilization Datasets Classification of service Medical Mental Health Substance Abuse Employment Income Assistance Criminal Justice Benefit details Medicaid database provides cost of all services accessed across treatment settings (ER, inpatient, outpatient) Database provides minutes per month spent receiving each service (individual/group therapy, assessment, etc.) Database provides number of days each month receiving each service (residential, intensive inpatient, outpatient, detox, etc.) Database provides wages earned and hours worked each month Database provides amount of assistance provided through a variety of programs (SSI, TANF, refugee assistance, etc.) Database provides days of contact with the justice system for felonies, misdemeanors, and days of incarceration The aim was to assess and compare the amount of services used and income earned pre- and posttreatment. The term index treatment indicates the IPP treatment that varied in level of gender-sensitivity and occurred at Month 25 of each individual s study period. 12

26 Procedure Cost Assessment of Index Treatment Medicaid reimbursement rates were used to calculate the cost of the index, IIP treatment episode. Washington State reimbursed programs $90.18 per patient per day in treatment in Each patient s length of stay in IIP treatment was calculated using reported admission and discharge dates and the number of days was then multiplied by the reimbursement rate to arrive at the total cost of treatment. The rate of reimbursement was adjusted for inflation to represent 2011 dollars so that the costs of treatment could be directly compared to the benefits across cohorts participating in treatment between 2005 and This adjustment was made using the Bureau of Labor Statistics consumer price index inflation calculator and the value of per diem reimbursement for IIP treatment arrived at $92.89 (BLS, 2014). Some participants were documented as participating in treatment for less than one day (n = 118). These individuals were excluded from cost, benefit, and cost-benefit analyses as they did not incur any costs during treatment. Assessing the Monetary Value of Outcomes The cost of participation in IIP was compared to the change in each patient s service utilization and earned income. Using rates of reimbursement provided by Medicaid, earned wages, income assistance totals, and estimates of the cost of criminal justice services, the cost of all services detailed in Table 3 can be totaled monthly across the four-year study period. The mental health and substance abuse datasets described in Table 3 were not utilized as the value of these services was represented in the medical dataset. The healthcare utilization dataset was already expressed in dollars per month of Medicaid reimbursement. The following service 13

27 domains were covered by the healthcare utilization dataset: general medical, pharmacy, mental health, chemical dependence, and detox. Within each service domain, the setting of service delivery was also noted (emergency room, inpatient, outpatient, nursing home, or unknown). To arrive at total healthcare utilization per month, all services across all settings were totaled per patient, per month. Services within substance abuse, mental health, and general medical services were also totaled and examined separately. Similarly, the employment dataset provided hours worked and wages earned each month. The economic assistance dataset detailed monthly monetary values for assistance received across the following categories: consolidated emergency assistance, diversion cash assistance, food assistance, income via the Alcohol and Drug Abuse Treatment Support Act (ADATSA), medical assistance, social security supplement, Temporary Assistance for Needy Families (TANF), general assistance, and refugee cash assistance. Finally, criminal justice datasets included both the number of days incarcerated and number of arrests, detailing both misdemeanors and felonies, for each patient, each month. To estimate the cost of incarceration, state jail and prison costs were ascertained from Washington state reports (Albert, 2010; Department of Corrections, 2014). According to Washington State Department of Corrections, individuals sentenced to more than one year and one day of confinement are placed in prisons rather than county jails, and so the higher per diem rate was applied for individuals with 367 or more days of incarceration. Crime-specific rates previously defined in the literature were used to calculate the court, sheriff s office resources, and victimization costs for individual s arrests (Aos, Phipps, Barnoski, & Lieb, 2001; McCollister, French, & Fang, 2010; Miller, Cohen, & Wiersema, 1996). For additional details about how these criminal justice rates were determined, see Appendix D. 14

28 Benefits were determined by examining the change in service utilization (health, criminal justice, economic assistance) and in earned income. For service utilization, the monetary outcomes for the posttreatment period were subtracted from the values for the pretreatment period, as it was expected that fewer services would be used after treatment. Pretreatment earned income was subtracted from posttreatment earnings because it was anticipated that individuals would earn more after successful treatment. Monetary values provided or estimated for all benefit domains (service utilization and earned income) were adjusted for inflation, separately for each year, so that all cohorts represented 2011 dollars. The Consumer Price Index was used to make adjustments for most domains, but indices indicating inflation rates specific to medical care services were used in adjusting these values (BLS, 2014). As 48 months of data were provided and Month 25 was the treatment month, an additional month (Month 49) was created by averaging the last three months of each individual s study period. This allowed for comparison of 24 months before and after treatment as participants could have had limited opportunity to maintain employment or become involved in the criminal justice system during Month 25. Using the treatment month to calculate benefits could misrepresent of the effects of treatment (underestimate earned income or overestimate service utilization benefit). Cost-Benefit Analysis As mentioned previously, one goal of cost-benefit analysis is to compare the monetary value of resources invested in gender-sensitive substance abuse treatment and the monetary outcomes or benefits, which include the value of service usage and earned income two years pretreatment versus two years posttreatment (Yates, 1999). The value of gender-sensitive, intensive inpatient treatment (in this case the number of days patients participated in treatment 15

29 multiplied by the daily Medicaid reimbursement rate) was compared to each individual s change in service usage and earned income pre- versus posttreatment, using the database information described above. Net benefit values were calculated by subtracting the total cost of treatment from the total benefits and from each specific benefit for earned income or service utilization. The latter specific net benefit values assumed that one benefit domain (healthcare utilization, criminal justice involvement, economic assistance, or earned income) was the only outcome of all treatment and the entire cost of treatment is being compared to that particular monetary benefit. Benefits and net benefits for men and women in the parenting or nonparenting groups were compared for programs of different levels of gender sensitivity. The intention was to make these comparisons using hierarchical linear modeling in SPSS (IBM Corp., 2011). This type of analysis was thought to be more appropriate than more basic forms of linear regression due to the nonindependence of participants given that they were participated in one of four levels of gender-sensitive treatment (Garson, 2013). Hierarchical regression analyses were also used to examine the relationship between costs, benefits, net benefits and sample characteristics. In these models, the outcome variable was benefits or net benefits and factors included parenting status, gender, and level of gender-sensitive treatment. Additional covariates were accounted for, including severity of substance abuse at intake and age of patient, as these variables could have an impact on outcomes and have been shown to be significantly different across treatment groups previously (Hornack & Yates, 2014). Marital status and satisfaction variables were used to create a variable to indicate the presence of marital support, which has been shown to be a positive indicator for treatment effectiveness (Ellis, 16

30 Bernichon, Yu, Roberts & Herrell, 2004). A variable to indicate whether a participant was part of the study before or after the economic downturn in 2008 was also created. The financial crisis began in September 2008 and likely affected the service utilization and earned income of the participants falling at the end of data collection (Mathiason, 2008). As groups in this study were unbalanced, nonparametric statistics such as the Mann-Whitney U test were used to compare benefits/cost ratios among groups (Yates, 1994). Kruskal-Wallis ANOVA (another nonparametric statistic) analyses and parametric ANOVA tests were used to further explore the hypotheses put forth regarding membership in parenting, gender, and treatment level groups. 17

31 CHAPTER 3 RESULTS Analysis of Possible Covariates Kruskal-Wallis ANOVA tests found significant differences among the gender-sensitive treatment levels in patient age, X 2 (3, n = 14,596) = 599.0, p <.01, 2 =.04, and reported alcohol and drug problem days in the month prior to entering IIP, X 2 (3, n = 13,941) = 678.5, p <.05, 2 =.05. For this type of analysis, the calculated eta-squared represents a small effect (Hall & Salkind, 2008). Specifically, individuals admitted to the most gender sensitive treatment were significantly younger (p <.001) than those receiving lower levels of gender sensitivity. Pairwise comparisons of drug problem days revealed no significant differences between specific treatment levels after Bonferroni correction to control for alpha inflation (this method was employed for all pairwise comparisons henceforth). Daley et al. (2000) examined additional covariates in multiple regressions of treatment cost-benefit, including employment status and ethnicity. Chi-square analyses indicated that the percentage of unemployed individuals, X 2 (3, n = 14,564) = , p <.001, and the ethnic makeup of groups across levels of gender-sensitive treatment were significantly different, X 2 (3, n = 14,596) = 2,889.8, p <.001. More specifically, individuals in gender-sensitive levels 2 and 4 were more predominately Caucasian and those in treatment level 3 were more likely to be employed at intake. This finding suggested that these were important covariates to add to later models. 18

32 Analyzing the Cost of Gender-Sensitive IIP Treatment It was important to take note of patients length of stay given how the cost of IIP treatment was derived. Unexpectedly, the average cost of treatment was significantly lower for individuals in the highest level of gender-sensitive treatment (level 4), as was their average length of stay. Table 4. Mean Length of Stay (LOS) and Cost of Index Intensive Inpatient Program, by Level of Gender Sensitivity Gender Sensitivity n LOS Mean Minimum Maximum SD 1. Low 4, $2, $92.89 $ 5, $ Medium Low 5, $2, $92.89 $14, $1, Medium High 2, $2, $92.89 $ 5, $ High 2, $1, $92.89 $ 5, $ The values in Table 4 and all analyses that follow do not include the 118 individuals who did not remain in treatment for at least one day, meaning that their treatment cost was zero. Also excluded from all analyses are individuals who died during the follow-up period (n = 237); additional details about these individuals can be found in Appendix F. These excluded individuals comprised approximately 2.3% of the overall sample. Kruskal-Wallis ANOVAs compared costs of IIPs differing in gender sensitivity, X 2 (3, n = 14,595) = 1,817.4, p <.001, 2 =.12, and length of stay, X 2 (3, n = 14,596) = 1,817.3, p <.001, 2 =.12. Pairwise comparisons indicated significant differences in cost among all treatment groups other than Levels 1 and 3 (p <.001). All levels of gender-sensitive treatment had significantly different stays, with treatment Level 4 being the shortest stay (p <.001). The cost of the index treatment was significantly more for men and for nonparents with respective statistical output of U (14, 596) = 20,220,516.0, z = -15.7, p <.001, r =.13 and U (11,561) = 19

33 15,818,579.0, z = -4.97, p <.001, r =.05. The effect size, r, was calculated using rank-biserial correlation and indicates a small effect (Willson, 1976). Benefits of Gender-Sensitive Treatment The total services used by each patient were totaled for each month of the study and are represented in millions of dollars in Figure Dollars in millions (5.0) (10.0) Month of Study Period Figure 1. Stacked Bar Graph Displaying Totals Across All Individuals, Each Month with Benefit Domains Labeled. It is evident that criminal justice services compose a large portion of public services used, although the cost of these services appears to decline over the last several months of the followup period. Healthcare utilization costs appear higher when comparing posttreatment to pretreatment, while income assistance accounts for a relatively smaller portion of the costs across time. Earned income is represented in the graph as negative or below the x-axis because this value is not considered a societal cost. Earned Income Health Care Utilization Criminal Justice Economic Assistance Service utilization and earned income benefits were compared using descriptive measures. Criminal justice involvement decreased over the two years after treatment to yield an average benefit of $ per patient (n = 14,596). In contrast, average economic assistance and healthcare utilization increased after treatment, generating negative benefit values of -$1,

34 and -$1, per patient (n = 14,596), respectively. The average patient (n = 14,596) earned less after treatment, -$ Visual examination of mean benefit across subgroups (prior to significance testing) also suggested that parenting women, and particularly those receiving higher levels of gender-sensitive treatment, consistently appeared to have less negative average benefit than other groups of participants. For specific values and graphical analyses across these benefit domains, see Appendices E and G. Average values of total benefit (sum of healthcare utilization, economic assistance, criminal justice, and earned income benefits) were negative, meaning earnings decreased and service utilization increased after treatment, for all levels of gender sensitive treatment [see Appendix H for specific values]. A Kruskal-Wallis ANOVA found significant differences in total benefits between levels of treatment, X 2 (3, n = 14,595) = 23.74, p <.001, 2 =.002, with pairwise comparisons indicating that gender-sensitive treatment higher than than Level 1 yields significantly less negative benefit values. However, no differences were found between Levels 2, 3, and 4 in total benefit. Multivariate Analyses of Benefits Hierarchical linear modeling was used to statistically analyze a data structure where patients were nested within gender-sensitive treatments. However, intra-class correlations (ICC) calculated from the initial null models examining benefits and net benefits revealed values ranging from.001 to.01, meaning that only.1 to 1% of variance in the model would be associated with treatment group differences. Lee (2000) suggested that with ICC values less than 10%, one could assume there is no meaningful nesting effect; therefore, analysis proceeded with alternate significance tests. 21

35 Hierarchical multiple regressions tested the hypothesis that an individual s level of service utilization or earned income benefit was a function of the three primary dependent variables: level of gender-sensitive treatment, gender, and number of children (a continuous variable, ranging from zero to thirteen). Gender-sensitive treatment level was the first variable entered, followed by gender, total children, and marital support status. Finally, interactions between gender-sensitive treatment, gender, total children and marital support status were added to regression models. This entry order was consistently used for all regression analyses. To avoid potentially problematic high multicollinearity, the variables were centered prior to the creation of the interaction terms (Preacher, 2003). For total healthcare utilization benefit, the key variables and interaction terms accounted for a significant proportion of the variance, R =.041, R 2 =.002, F (8, 9,164) = 2.70, p <.05, 2 =.001. Again, eta-squared is the calculated effect size and in this instance indicated a small effect (Fritz, Morris, & Richler, 2012). The model suggested that having a higher number of children (β = 317.0, t = 2.49, p <.05) led to less of an increase in healthcare utilization after treatment. Specific areas within healthcare utilization benefit were also examined using hierarchical regression, namely change in substance abuse services, mental health services, and general medical services used. The overall model for substance abuse service benefit was significant, R =.051, R 2 =.003, F (4, 9,164) = 5.93, p <.001, 2 =.002, and indicated more negative benefit values if an individual was female (β = , t = -3.76, p <.001) and received a higher level of gender-sensitive treatment (β = , t = -2.15, p <.05). Change in mental health treatment services used after IIP treatment was not predicted by the model, R =.031, R 2 =.001, F (8, 9,164) = 1.10, p =.359. Finally, the model approached significance for predicting general 22

36 medical service-only benefit, R =.040, R 2 =.002, F (8, 9,164) = 1.80, p =.072, although the number of children variable was significant (p <.05) and indicated that being a parent decreased use of medical services after IIP treatment. The hierarchical regression model significantly predicted earned income benefit, R =.049, R 2 =.002, F (8, 9,163) = 2.71, p <.01, 2 =.002. Having a supportive marriage led to decreased earnings after treatment (β = -1,530.4, t = -2.93, p <.01) while being a female in higher levels of gender-sensitive treatment predicted greater increases in earnings during the follow-up period (β =822.8, t = 2.19, p <.05). The best fitting model for predicting economic assistance benefits involved all primary variables and interactions, R =.083, R 2 =.007, F (8, 9,163) = 8.0, p <.001, 2 =.01. Female gender (β = 293.2, t = 2.78, p <.01) and reporting a greater number of children (β = 171.8, t = 5.11, p <.001) predicted greater decreases in economic assistance after treatment. The interaction of gender and total children was also significant, β = 276.5, t = 3.99, p <.001, suggesting that women with more children garnered higher economic assistance benefit. No significant predictors were revealed for criminal justice benefit, R =.029, R 2 =.001, F (8, 9,164) =.96, p =.467. Net Benefits of Gender-Sensitive Treatment The cost of treatment was subtracted from each benefit, which represented the change of specific service utilization and earned income in the 2-year pretreatment and posttreatment periods, to arrive at the specific net benefit. Given that many of the benefit values were already negative, average net benefit values across service utilization and earned income were all negative as shown in Table 5. 23

37 Table 5. Mean Net Benefit For All Patients Across Service Utilization and Earned Income Domains, n = 14, 595 Benefit Domain Mean SD Healthcare Utilization -$ 4, $ 17, Criminal Justice -$ 1, $ 161, Economic Assitance -$ 3, $ 4, Earned Income -$ 2, $ 17, Mean, specific net benefits across gender, parenting status, and gender-sensitive treatment level were calculated. Criminal justice was the only domain that showed positive average net benefits for subgroups [see Appendix I for specific values]. A series of Kruskal-Wallis ANOVA analyses found significant differences (p <.001) across levels of gender-sensitive treatment in every area of net benefit. Pairwise comparisons found that those in the most gender-sensitive treatment (Level 4) had significantly less negative total healthcare utilization, earned income, and economic assistance net benefit compared to those in less intense gender-sensitive treatment; see Appendix J for additional details. Multivariate Analyses of Net Benefits The net benefits (the total cost of treatment subtracted from each benefit value) for healthcare utilization, earned income, economic assistance, and criminal justice were entered as dependent variables into hierarchical regression models with independent variables (gender, total children, level of gender-sensitive treatment, marital support) and their interactions. Less negative healthcare utilization net benefit was significantly related to having more children, β = 315.1, t = 1.05, p <.05; the simpler regression model (without interaction variables) was significant, R =.033, R 2 =.001, F (4, 9,194) = 2.52, p <.05, 2 =

38 Earned income net benefit was predicted by the overall model, R =.051, R 2 =.003, F (8, 9,163) = 2.97, p <.01, 2 =.002. The gender-sensitive treatment by gender interaction term was significant (p <.05), whereby women showed greater increases in earned income net benefit with more gender-sensitive treatment. Having a supportive marriage predicted more negative earned income net benefit, β = , t = -2.90, p <.01. Interaction variables also improved the ability of regression models (R 2 change =.002, F change = 4.37, p <.01) to predict net benefit for economic assistance, R =.095, R 2 =.009, F (8, 9,163) = 10.5, p <.001, 2 =.008. Gender, total children, and their interaction were all significant predictors (p <.001) and indicated that females with a higher number of reported children have greater economic assistance net benefit. Finally, hierarchical regression analysis found that none of the variables predicted criminal justice net benefit, R =.029, R 2 =.001, F (8, 9,164) =.97, p =.45. Total Net Benefit of Gender-Sensitive Treatment Total net benefit was calculated by subtracting the cost of IIP treatment from the sum of all service utilization (criminal justice, economic assistance, and total healthcare utilization, which included mental health, substance abuse, and general medical services) and earned income benefits. This is averaged across levels of gender-sensitive treatment in the following figure. Figure 2. Bar Graph Demonstrating the Mean, Total Net Benefit Across Levels of Gender- Sensitive Treatment, with Confidence Interval Bars. 25

39 For all levels of gender sensitivity, the average total net benefit is a negative value, meaning the change in benefits or earned income did not exceed treatment costs. The average total net benefit approaches zero with higher treatment gender-sensitivity (Levels 3 and 4) but the figure indicates that a cumulative thousand dollars or more were lost per patient by two-year follow-up. A Kruskal-Wallis ANOVA demonstrated a significant difference among treatment groups, X 2 (3, n = 14,595) = 25.66, p <.001, 2 =.002. Pairwise comparisons indicated significantly less negative net benefit (p <.001) for gender-sensitive treatments higher than Level 1. See Appendix K for additional total net benefit descriptive information. Multivariate Analyses of Total Net Benefit First, a factorial ANOVA did not reveal a significant three-way interaction between gender-sensitive treatment level, gender, and parenting status, F (3, 11,286) = 1.36, p =.158, for total net benefit. Two-way interactions also failed to reveal significant results [see Appendix M]. Next, a hierarchical multiple regression analysis found that gender, number of children, marital support, level of gender-sensitive treatment, and interaction variables did not significantly predict total net benefit, R =.028, R 2 =.001, F (8, 9,163) =.906, p =.510. A second hierarchical regression was performed, taking into account a number of potential covariates of total net benefit. In this case, a third step added drug problem days prior to treatment, age, and a variable indicating treatment during the economic recession, as a Mann-Whitney U analysis showed significantly more negative net benefit for those participating in treatment after the recession s onset. Additional information about the recession variable can be found in Appendix L. The fourth step added ethnicity and unemployment variables. These steps failed to produce significance in the overall model, R =.046, R 2 =.002, F (12, 8,537) =.1.50, p =.117, and no 26

40 independent variables significantly predicted total net benefit [See Appendix N for specific statistical output]. 27

41 CHAPTER 4 DISCUSSION In today s healthcare climate, an emphasis on minimizing costs and the need to demonstrate efficacy of services make information about the costs and benefits of gendersensitive treatment all the more valuable. Research suggests that specialized services in substance abuse treatment can be helpful to women; however, Grella (2007) pointed out that the additional cost often associated with providing of gender-sensitive treatment makes these services vulnerable to budget cuts. The current project attempted to examine the costs, benefits, and cost-benefit of gender-sensitive treatment to determine whether monetary outcomes might eclipse the cost of treatment, and for which individuals. Does Gender-Sensitive Treatment Cost More? Significant differences in IIP cost were not anticipated across gender-sensitive treatment groupings. A significant difference in treatment cost (and length of stay) was shown across the levels of gender-sensitive treatment, with the lowest cost occurring for the highest level of gender sensitivity because average length of stay was only 18 days. As individuals participating in higher levels of gender sensitive treatment were found to be significantly younger, one potential reason for a shorter length of stay was that younger patients had fewer co-occurring issues because their substance abuse history was less prolonged. Although patient s age was seemingly related to the cost of treatment, it was not a significant predictor of net benefit in regression models. Shorter patient stays in treatment Level 4 (the most gender-sensitive) may have also been driven by programs, recognizing that the additional costs required for providing gender-sensitive treatment may not be wholly covered by standard Medicaid reimbursement 28

42 (Hornack & Yates, 2014). So, shorter stays may be standard in an effort to minimize program income deficits. It was posited that the treatment cost for women might be higher (though not significantly) due to their need for more intensive services (Reed & Mowbray, 1999). Instead, comparative analyses demonstrated significantly lower treatment costs for women and for parents, indicating they had a shorter length of stay. This difference could potentially be related to the barriers for women entering treatment that have been identified in the literature. For instance, women and parents may have responsibilities in the home (e.g., care for dependents) and therefore stay in inpatient treatment for shorter periods. Effect sizes for all of the statistical comparisons around cost of treatment were small. Are Specific Benefits and Net Benefits of Gender-Sensitive Treatment Greater? A significant interaction predicted less negative earned income benefit and net benefit for female patients receiving higher gender sensitivity in treatment. Otherwise, level of treatment gender-sensitivity did not predict specific benefits or net benefits. Earned income benefit and net benefit was also found to be more negative for individuals in supportive marriages, possibly suggesting that those with supportive spouses feel less of an impetus to be a primary breadwinner after treatment. For total healthcare utilization (including general medical, mental health, and substance abuse services), having more children predicted greater benefit and net benefit. This relationship remained when substance abuse and mental health treatment services were removed and general medical service benefit was examined independently. While the regression model was not 29

43 significant for mental health service benefit, decreased (more negative) substance abuse treatment benefit was significantly related to female gender and participation in higher levels of gender-sensitive treatment. While this does not appear to be a positive outcome, it suggests that individuals in gender-sensitive treatment are more likely to engage in continued treatment following IIP treatment, which is consistent with previous findings (Claus et al., 2007). Continuing care after inpatient treatment has been shown to have positive implications for longterm outcomes. This relationship could also be related to shorter length of stay for individuals in more gender-sensitive programs; if programs are adept in enrolling patients in step-down care, it may be appropriate for them to transition to the least-restrictive setting sooner rather than later. Larger economic assistance benefit and net benefit was predicted by female gender and having more children, and by the interaction of these two variables. While significant, the models only accounted for.1 to.9% of the variance in benefits. Criminal justice benefit and net benefit was not predicted by any of the regression models. Although the total cost of treatment was subtracted from each specific benefit to calculate net benefit rather than subtracting a portion of the treatment cost associated with each benefit, it is not expected that using a partial cost would have affected the relationship between outcomes and treatment gender sensitivity, gender or parenting status. It was hypothesized that women and parents of both genders would display greater benefits and net benefits across all domains, particularly if they received more gender-sensitive treatment, but this was not consistently the case. Female gender, being a parent, and the interaction of gender with treatment gender sensitivity predicted positive impacts on benefits and net benefits for different domains. This finding is in contrast with previous reports of lower 30

44 benefits for parenting women following traditional substance abuse treatment compared to men or women with no children (Gerstein et al., 1994). However, these relationships are consistent with the hypothesis that parenting women would benefit more from the emphasis on gendersensitive treatments that some IIPs in this study offer. It is apparent in the results that many values are negative, meaning that individuals used more services after treatment than before (and often earned less after treatment than before). Therefore, the costs of treatment for many in this sample were not offset by benefits generated from changes in service utilization or earned income as expected. It could be that using more services in healthcare and income support is a good outcome in that individuals are engaging in health promotion and are potentially less likely to seek illicit income. The largest benefits and net benefits from treatment were generated through less criminal justice involvement (see Table 5), consistent with previous studies reports that cost-benefit stemming from substance abuse treatment is often largely due to reductions in criminal justice (McCollister & French, 2003). Is the Total Net Benefit of Gender-Sensitive Treatment Greater? Statistical tests did note significant differences in total benefit and total net benefit among levels of gender-sensitive treatment; specifically, individuals in treatment Level 1 had significantly more negative total benefit and total net benefit compared to those in more gendersensitive treatments. A factorial ANOVA did not find significant three-way or two-way interactions between gender, parenting status, and gender-sensitive treatment level related to total net benefit. In hierarchical regression analyses, none of the models approached significance and gender, parenting status, and level of gender-sensitive treatment received were not related to 31

45 total net benefit. Possible explanations for findings contrary to the original hypotheses are offered below. Project Limitations The above conclusions should be viewed with the project s limitations in mind. For instance, with regard to criminal justice, others have asserted that subsequent arrest rates are not wholly accurate in reflecting underlying offending (Zedlewski, 2010). Both indices of criminal behavior in this study, state-documented arrests and incarcerations, are dependent on identified crimes. Victimization costs in the current project were adjusted for the likelihood of multiple victimizations per each arrest (Aos et al., 2001; Miller et al., 1996). Although depending on selfreport to gather criminal activity information presents difficulties as well, including misremembering or demand characteristics, Daley et al. (2000) used this method to record crimes that are both more common among females and often unidentified, such as prostitution or drug solicitation. Their study was able to identify substantial net benefit (ranging from $3,072 to $32,772) when accounting for criminal activities avoided after substance abuse treatment. Additionally, some costs or potential benefits were not accounted for in the current study because the overall aim was to focus on and compare the societal costs across each of the service utilization domains. Health and quality-of-life improvements for the clients and their families is one area of benefit that was not assessed in the present study and could have proven gendersensitive services to be more cost-beneficial. One unique characteristic of the participants in this study is the number of American Indian individuals represented. While the proportion of Native Americans in the U.S. population is approximately 1%, the study sample is 6.4% Native American (Shrestha & Heisler, 2011). In 32

46 terms of drug abuse and dependence, epidemiological studies have shown higher incidence rates among American Indian populations. Specifically, in % of Native American women reported abuse of drugs and alcohol compared to 6.3 % White, 4.5% Black, 4.4% Hispanic, and 3.4% Asian women (SAMHSA, 2005). Although ethnicity was not significant in the regression model, cultural factors could have influenced outcomes in a way that was not tested. Moreover, one of the participating programs in was an Indian Health Board. Future studies could examine whether a combination of gender and cultural sensitivity has a greater impact on outcomes. The current project did not find positive net benefits resulting from participation in gender-sensitive treatment, contrary to previous studies findings. There are a number of reasons that this could have occurred, including the aforementioned fact that this study did not estimate additional monetizable, positive outcomes (e.g., improvements in quality of life) as others in the literature have (French et al., 2000). Instead, administrative datasets provided specific instances of services delivered. This is also a distinction between the current project and the studies of French et al. (2002) and Daley et al. (2000), where self-report measures were used to assess benefits. Patients were asked to report on their income, healthcare utilization, criminal activity, etc. using standardized measures. While this method could provide information beyond that of a database, it is also susceptible to demand characteristics. Another key difference between this project and previous research is the study period s time frame, comparing two years before and after the index treatment. This is atypical of costbenefit studies, where shorter periods of service utilization are typically compared. For example, French et al. (2002) examined the 6 months before treatment initiation and used interpolation and 6-month follow-up data to estimate service utilization and earned income posttreatment. 33

47 Similarly, Gerstein et al. (1994) compared one year before treatment and then interpolated data from the follow-up interview (which occurred at varying intervals) to estimate 1-year posttreatment outcomes. It is possible that comparing the period just before treatment initiation, where service utilization is seen to increase dramatically while earnings decline, to a shortened period after treatment discharge is more likely to yield benefit. Lack of reduced healthcare utilization following treatment was a particularly surprising finding given it is a commonly reported in the literature. As IIP treatment is the most intense level of treatment that Washington state offers, even above residential treatment, it could be that this population s severe substance abuse issues and related medical maladies caused them to be less likely to show reduced service usage. It could also be true that service delivery has changed in the decade since other studies examining the cost-benefit of gender-sensitive treatment were published. As the system moves closer to universal healthcare, it could be that medical services are now arranged regularly as part of discharge planning and individuals are more aware of these entitlements. Again, while not considered a positive outcome when focusing on cost-benefit, individuals could be receiving much-needed healthcare services after IIP treatment and be taking a more proactive approach to self-care. Future Research into the Cost of Gender-Sensitive Treatment Although much has been learned in the last two decades about the importance of providing specialized substance abuse treatment services, there is still relatively little information about the cost and monetary outcomes of providing these services. Examination of the costeffectiveness of gender-sensitive treatment, comparing the cost of treatment to nonmonetary 34

48 outcomes, could be continued through the current project. Cost-effectiveness was studied with a subset of women in this sample previously, but larger-scale analyses could examine outcomes such as return to substance abuse treatment or engagement in step-down care (Hornack & Yates, 2014). Related future research could also examine variables not included in this study. For instance, multiplier effects of decreased substance use within the family following treatment of one individual could occur. The intergenerational effects of gender-sensitive treatment could also be valuable to explore, as parental substance abuse has been shown to impact the well-being of children (Merikangas, Rounsaville & Prusoff, 1992). More proximally, because assessment of parenting skills and relationship-focused groups are routinely provided within gender-sensitive treatment, measuring an outcome like parental effectiveness or reduced family conflict may be of future interest. Another prospect for ongoing endeavors would involve examining the costs, costeffectiveness, and cost-benefit of treatment careers rather than a discrete treatment episode, as Hubbard and French called for in their 1991 publication. It is known that substance dependence is a chronic disease and often requires multiple courses of treatment. Looking at the frequency and type of treatment required could provide additional information about the effectiveness or benefits related to gender-sensitive treatment. Conclusion The findings of this project do not present a clear picture of the relationship between gender, parenting status, gender-sensitive treatment, and benefits or cost-benefit of treatment. Individuals participating in gender-sensitive treatment above the first level of gender sensitivity 35

49 demonstrated significantly less negative total benefit and net benefit. Simultaneously, individuals in the highest level of gender-sensitive treatment had shorter IIP stays (approximately 2.5 weeks versus slightly over 3 weeks) and therefore cost significantly less to treat. These findings suggest similar or superior benefits and cost-benefit for gender-sensitive treatment with smaller doses of treatment. In regression analyses, however, the level of gender-sensitive substance abuse treatment received did not reliably impact benefits or net benefits, while being female and having one or more children both exhibited significant relationships to positive outcomes. Altogether, appears that it is imperative to continue cost-inclusive research and clinical focus on these particular populations for the patients themselves, their families, and society at large. 36

50 APPENDIX A FIGURE DEMONSTRATING POTENTIAL TRAJECTORY FOR HEALTH CARE SERVICE USEAGE The figure below demonstrates a possible relationship between receipt of substance abuse treatment and health care costs. Cost offset or cost savings related to following substance abuse treatment has been shown in numerous studies of cost-benefit (Holder, 1998; Gentilello et al., 2005). Figure 3. An Illustration of Potential Offset Health Care Savings Resulting from Substance Abuse Treatment (Holder, 1998). In terms of this project, the expectation is that the comparison of pretreatment and posttreatment healthcare costs will differ depending on whether an individual participated in higher levels of gender-sensitive treatment, is a women, or is a parent. 37

51 APPENDIX B ILLUSTRATION OF HYPOTHESIZED BENEFITS AND COST-BENEFIT The figure below demonstrates the hypothesized relationships between gender, parenting status, gender sensitivity of treatment, and benefits or cost-benefit values. Parent Nonparent Male Female Male Female 1. Low 2. Medium Low 3. Medium High 4. High Figure 4. Hypothesized Value of Benefits for Each Level of GS Treatment and Participant Category, Larger Dollar Signs Indicating Greater Benefit as a Result of Gender-Sensitive Treatment. As discussed in the first chapter, it is anticipated that parents and women will demonstrate greater benefit from participation in gender-sensitive treatment compared to nonparents and men. Larger dollar signs indicate a larger benefit (the difference between pre- and posttreatment) and a larger cost-benefit or net benefit. 38

52 APPENDIX C MEASURES UTILIZED IN DEVELOPING THE GENDER-SENSITIVE SCALE Information gathered via staff and patient interviews and by the research team s observations was used in ranking programs by their gender sensitivity (Tang et al., 2012). The Program Director Interview included 102 items divided into 7 sections (program s philosophy, how patients were admitted, children s services offered, the program s challenges, program costs). The director was also given a staff matrix to complete which asked for information on each applicable staff member to be filled into the grid. Years of experience, salary, time dedicated to the IIP program, and other benefits were inserted into the grid. The Clinical Director Interview includes 170 items and is divided into 11 sections, asking about treatment philosophy, the patient population, assessment, treatment planning, services available to patients, services available to children and family, discharge planning, posttreatment housing services, and continuing care services. The Counselor Interview included 102 items, and asked questions specifically about patient access to counseling, patient access to their children, continuing care services, patient satisfaction, barriers to treatment, and general program environment details. Within the much larger sample, 76 women patients were interviewed while in treatment at one of the 13 programs visited. Questions in the Patient Interview asked patients to detail frequency and amount of time spent in individual counseling, family or couples counseling, group therapy or education (mixed or coed), group therapy or education (women only), medication management, other medical services, and other appointments (e.g., legal, child custody, housing, etc.). Space was also provided as opportunity to list other treatment-related activities. Specifically, the interview asked for the time spent each day on treatment activities, 39

53 how many times per week they completed each activity, and if there was transportation time or fees involved. There was also a section in which to detail any wages or benefits given up while part of inpatient treatment. Two interviewers at each site rated programs on 33 items using a 5-level scale, with 1 indicating low and 5 indicating high. This Observation Protocol included items such as how institutional versus homey the facility seemed, how much privacy the women had, and gave an overall rating of gender sensitivity. The two interviewers developed their own ratings for each variable separately and then came together to reach consensus. 40

54 APPENDIX D CALCULATION OF CRIMINAL JUSTICE COSTS Both datasets detailing criminal justice information for participants were originally represented in nonmonetary units. A Washington State Patrol dataset provided number of arrests each month across a number of misdemeanor categories, including: assault/harassment, theft/burglary, sex crimes, domestic violence, child abuse, trespassing, reckless driving, vehicle license-related crimes, weapons-related crimes, probation-related crimes, failure to comply, driving under the influence. Information for felony offenses was also provided, including: assault/harassment, theft/burglary, sex crimes, domestic violence, child abuse, custody violation/nonsupport crimes, alcohol/drug-related crimes, trespassing, reckless driving, vehicle license-related crimes, weapons-related crimes, probation-related crimes, murder/homicide, failure to comply, and driving under the influence. Previous literature has the determined taxpayer costs (police and sheriffs offices, superior court and county prosecutor costs) and victimization costs associated with specific misdemeanors and felonies (Aos, Phipps, Barnoski, & Lieb, 2001; Miller, Cohen, & Wiersema, 1996; McCollister, French & Fang, 2010). The table below details the available estimated societal costs associated with each type of crime. Table 6. Crime-Specific Societal Cost Estimates Type of Crime Police and Court Costs Victimization Costs Total General Misdemeanors $ 1, $ 1, Misdemeanor Theft $ 4, $ $ 5, Drug-Related Felony $ 4, $ 4, Aggravated Assault $ 24, $ 11, $ 36, Murder/Manslaughter Theft-Felony $ 27, $ 8, $ 36, Sex Crime-Felony $ 34, $ 135, $ 169, Murder/Manslaughter $ 267, $ 2,326, $ 2,602, Note: All values above were previously adjusted to take into account inflation and reflect 2011 dollars. 41

55 When more than one source provided an estimate for a specific crime, a median value was calculated. The general misdemeanor value was used in calculations to determine the cost of arrest for misdemeanor crimes not defined in the literature. For those felonies in the database with no previously defined costs, the median value for felonies ($4,670.15) as defined by Aos et al. (2001) was utilized. Values for victimization costs were provided by Aos et al. (2001) and Miller et al. (1996); these estimates included not only costs related to a victims medical/mental health expenses and property loss, but also included adjustments for series victimization or the likelihood that multiple victimizations likely occurred before an arrest took place. These prior studies also noted the types of crimes that are classified as victimless (e.g., trespassing, probation violations). No victimization costs were applied to these specific crimes. The Department of Corrections dataset detailed number of days each participant was incarcerated, each month. As mentioned in the body of this document, Washington State s Department of Corrections reported that individuals sentenced to at least one year and one day of confinement are sent to prisons while those with lesser sentences are incarcerated in county jails (DOC, 2014). If an individual s incarceration was documented as being 367 contiguous days or less, the median rate of $66.44 for county jails in Washington state was used (Albert, 2010). For more than 367 days, the prison rate reported by Washington state for fiscal year 2011 ($94.40) was used to calculate the cost of each individual s incarceration (DOC, 2012). 42

56 APPENDIX E GRAPHICAL ANALYSES OF BENEFITS ACROSS THE STUDY PERIOD The graphs below examine the mean healthcare utilization, economic assistance, criminal justice involvement, and wages earned by participants who received different levels of gendersensitive treatment. Figure 5. Line Graphs Representing Mean Economic Assistance, Healthcare Utilization, Earned Income, and Criminal Justice Involvement by Participants in Each Level of Gender-Sensitive Treatment Across the Study Period. These graphs show varying trends over time for service utilization and earned income. The index, intensive inpatient treatment episode (Month 25) is demarcated by a reference line in 43

57 each graph. Average receipt of economic assistance increases around the time of treatment entry and then declines, but remains higher than pretreatment assistance. Those individuals receiving the most gender-sensitive substance abuse treatment (Level 4) consistently receive the most income supports. Men and women who participated in the lowest level of gender-sensitive treatment use more healthcare services on average across the study period. All treatment groups appear to experience a spike in health service utilization at the index treatment, which then declines, but does not fall below pretreatment levels of usage. As might be expected, average earned income declines sharply around the point of substance abuse treatment entry and do not fully return to pretreatment values. Average costs related to criminal justice involvement are highly variable. The average cost for all groups declines in the two-year follow-up period, falling below pretreatment averages. Healthcare Utilization As noted previously, the Medicaid healthcare reimbursement for each individual, each month was provided. The graph below represents the average reimbursement for healthcare utilization over the entire study period. Figure 6. Line Graph Representing the Average Medicaid Reimbursement of All Participants for Healthcare Services Each Month of the Study Period. 44

58 According to this graph, a noticeable spike in use of medical, mental health, and substance abuse services occurs around the time of entry to intensive inpatient treatment (month 25). While receipt of services is declining during the two-year follow-up period, it does not reach pretreatment levels of service utilization. In order to explore possible differences among male and female patients, another line graph was created to compare the average healthcare utilization across the 48 months of treatment. Figure 7. Line Graph Representing Average Medicaid Reimbursement for Healthcare Services by Gender of Participant. The trend for both males and females follows that of the overall sample in terms of average healthcare costs over the four-year study period. Most notable is the difference in service utilization between males and females. This is not a surprising finding as women are known to use significantly more medical services than men and because it has been shown that more medical problems accompany substance abuse for women (Owens, 2008; Najavits, Rosier, Nolan, & Freeman, 2007). As the study at hand is also interested in differences among parenting 45

59 individuals, a graph displaying the difference in healthcare utilization between parents and nonparents was generated. Figure 8. Line Graph Representing Average Medicaid Reimbursement for Healthcare Services by Parenting Status of the Participant. The above graph depicts the trajectory for parent and nonparent participants over the study period, which appears similar to that of the overall group. Individuals with no children seem to use slightly more health services at the initiation of the study and after treatment. Economic Assistance Averages over time for total income assistance received and TANF assistance were graphed to explore differences among subgroups of the larger sample. The graph below displays the mean receipt of economic assistance on a monthly basis across the four-year study. 46

60 Figure 9. Line Graph Representing the Average Economic Assistance Received by All Participants Each Month of the Study Period. This line graph suggests that average income supports increase sharply around the time of admission to intensive inpatient and then decline somewhat, but appear to remain well above the pretreatment level. Graphical representations of economic supports received by males versus females and parents versus nonparents were also generated. Figure 10. Line Graph Representing the Average Economic Assistance by Gender Over the Study Period. Given previous findings that women who enter substance abuse treatment have lower educational attainment and rates of employment than their male counterparts, it is not surprising to see that women in this sample consistently receive more economic assistance (Wong, Badger, Sigmon, & Higgins, 2002). It does appear that men exhibit a greater change in economic support before and after treatment compared to women, on average. 47

61 Figure 11. Line Graph Representing the Average Economic Assistance by Parenting Status. The graph above indicates that individuals with children receive more income assistance on average than do nonparents. The nonparenting group, however, appears to experience more of an increase in economic support when comparing the pre- and posttreatment period. Earned Income Another domain for potential benefit is employment and licit earnings after receiving successful substance abuse treatment. The graph below demonstrates the average earned income trend for all participants, across the 48-month study period. Figure 12. Line Graph Representing the Average Earned Income Received by All Participants Each Month of the Study Period. 48

62 It is seen that the average earnings of participants declines around the time of admission to IIP treatment and then increases in the follow-up period, although earnings never appear to reach the level of pretreatment. The graph below explores this trend by gender of the participant. Figure 13. Line Graph Representing the Average Earnings Received by Men and Women Across the Study Period. Men and women both experience a steep decline in earnings before entering substance abuse treatment. While men consistently earn more on average over time, women s earnings are seemingly closer to returning to pretreatment levels at the end of the study period. Figure 14. Line Graph Representing the Average Earnings by Parenting Status Across the Study Period. 49

63 On average, the parents who participated in this study earn more than those without children. Also, it appears that the parents were closer to earning the same level of income they did at the end of the study as they did at the beginning. Criminal Justice Involvement The fourth area that was examined for potential benefits following IIP substance abuse treatment was involvement in the criminal justice system (arrests and incarceration). Average criminal justice costs for all participants across the 48 months of the study are displayed in the graph below. Figure 15. Line Graph Representing the Average Criminal Justice Cost of All Participants Each Month of the Study Period. The trend above displays an increased involvement in the criminal justice system just before entry to IIP treatment, suggesting that the court may have mandated referrals to treatment. During the follow-up period, it seems that participants average criminal justice costs increase after treatment completion but stabilize and eventually decrease, possibly falling below the pretreatment level. The average pattern of criminal justice involvement by gender and parenting status is examined in the figures below. 50

64 Figure 16. Line Graph Representing the Average Criminal Justice Cost by Gender for Each Month of the Study Period. Males in the sample incurred more criminal justice costs on average. Both groups follow a similar pattern of average costs, with criminal justice involvement appearing to fall below pretreatment mean costs in the time after completion of substance abuse treatment. Figure 17. Line Graph Representing the Average Criminal Justice Cost by Parenting Status Each Month of the Study Period. The mean criminal justice cost is highly variable across the study period when comparing parents to nonparents. Based on the line graph, parents seem to have incurred slightly more criminal justice costs than nonparents on average. 51

65 APPENDIX F CHARACTERISTICS OF PARTICIPANTS WHO DIED DURING FOLLOW-UP PERIOD In this sample there were 237 individuals who died during the two years following IIP treatment and were excluded from analyses as a result. The table below details the gender of these participants and which treatment group they had participated in. Table 7. Frequency of Deceased Participants Across Levels of Gender-Sensitive Treatment Gender-Sensitive Treatment Level Gender I II III IV Total Male Female Total Comparably, 1 to 2% participants in each level of treatment died during the two-year follow-up period. More males than females died during this period, but the deaths are proportionally similar and represented 1.7% and 1.2% of these groups, respectively. 52

66 APPENDIX G DESCRIPTION OF MEAN BENEFITS ACROSS LEVEL OF GENDER-SENSITIVETREATMENT, GENDER, AND PARENTING STATUS Potential benefits of treatment (comparing months 1 through 24 and months 26 through 49) were examined within the areas of service utilization and earnings. The tables below display mean values for each benefit domain across key subgroupings, gender, parenting status, and the gender-sensitivity of treatment received. Table 8. Average Benefit Values for Healthcare Utilization Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low -$ 2, $ 2, $ 1, $ 1, Medium Low -$ 1, $ 2, $ 1, $ 2, Medium High -$ $ $ 1, $ 2, High $ $ 1, $ 7, $ 1, Parents display a pattern of benefits that approximately fits the study expectations in that males and females in higher levels of gender-sensitive treatment display greater benefit (although most individuals appear to be using more healthcare services after treatment than before on average). Also, parenting men exhibit greater average benefit compared to women with children, which is contrary to the expected finding. Nonparenting men and women do not uphold any of the anticipated healthcare benefit patterns by level of gender-sensitive treatment received. Table 9. Average Benefit Values for Economic Assistance Parent 53 Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low -$ $ $ 1, $ 1, Medium Low -$ 1, $ $ 1, $ 1, Medium High -$ $ $ 1, $ High -$ $ $ 1, $ 1,845.28

67 In visually examining the mean change in economic assistance received before and after treatment, it appears that parents receive less support than nonparents overall. Women with children seemingly exhibit the greatest average benefit in terms of economic assistance. None of the groups displays a clear relationship between economic assistance benefits and the level of gender-sensitive treatment received. Table 10. Average Benefit Values for Earnings Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low $ 1, $ $ $ Medium Low -$ 1, $ $ $ 1, Medium High $ $ $ $ High -$ 1, $ 1, $ $ 1, Female parents average benefits within the domain of earnings increases with receipt of more gender-sensitive treatment. Parents, more than nonparents, display higher average earnings following IIP treatment. Table 11. Average Benefit Values for Criminal Justice Services Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low $ 6, $ 2, $ 18, $ 4, Medium Low $ 1, $ 4, $ 2, $ Medium High $ 3, $ 3, $ $ 4, High $ 2, $ 1, $ 5, $ 4, Benefits related to criminal justice involvement are greater than those in any other domain. It is not evident, however, that criminal justice benefits follow any expected patterns across subgroups. 54

68 APPENDIX H DESCRIPTIVE INFORMATION FOR AVERAGE TOTAL BENEFITS ACROSS LEVEL OF GENDER-SENSITIVE TREATMENT When all benefits across healthcare utilization, economic assistance, criminal justice, and earned income were totaled, the average value for the overall sample was negative with great variability, n = 14,595, M = - $2,300.10, SD = $163, In examining the average total of all benefits (service utilization and earned income) across levels of gender-sensitive treatment, the values remain negative for all levels. The table below displays the average values for total benefits, or the sum of changes in service utilization and earned income when comparing the periods before and after IIP treatment. The gender sensitivity level of the index treatment is also noted below. Table 12. Average Total Benefit Values by Level of Gender-Sensitive Treatment Gender Sensitivity n Mean Minimum Maximum SD 1. Low 4,098 -$ 7, $13,868, $2,770, $252, Medium Low 5,236 -$ $ 5,244, $2,780, $120, Medium High 2,351 -$ $ 2,499, $ 375, $ 72, High 2,910 -$ $ 2,631, $2,770, $119, These negative values occurred because average healthcare utilization and economic assistance increased and earned income decreased on the whole after treatment. Again, however, great variability is seen in the data. 55

69 APPENDIX I DESCRIPTION OF MEAN NET BENEFITS ACROSS LEVEL OF GENDER-SENSITIVE TREATMENT, GENDER, AND PARENTING STATUS The net benefits of treatment (comparing the difference between months 1 through 24 and months 26 through 49, and then subtracting the cost of the index treatment) were examined within the areas of service utilization and earned income. The tables below display mean values for each net benefit domain across key subgroupings, gender, parenting status, and the gendersensitivity of treatment received. Table 13. Average Net Benefit Values for Healthcare Utilization Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low -$ 4, $ 4, $ 4, $ 3, Medium Low -$ 3, $ 4, $ 4, $ 4, Medium High -$ 3, $ 2, $ 4, $ 4, High -$ 1, $ 3, $ 9, $ 3, Average net benefits for healthcare utilization are all negative, indicating that there are no cost savings in this domain that are related to treatment. There are also no obvious differences between the values across different groups. Table 14. Average Net Benefit Values for Economic Assistance Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low -$ 3, $ 2, $ 3, $ 3, Medium Low -$ 3, $ 2, $ 3, $ 3, Medium High -$ 2, $ 1, $ 3, $ 2, High -$ 2, $ 2, $ 3, $ 3,

70 As with mean net benefit for healthcare utilization, all values for economic assistance were negative. No obvious trends were evident between the groups, aside from slightly higher net benefits for parenting women compared to other groups. Table 15. Average Net Benefit Values for Earned Income Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low -$ $ 2, $ 2, $ 2, Medium Low -$ 3, $ 1, $ 2, $ 3, Medium High -$ 2, $-1, $ 2, $ 2, High -$ 3, $ $ 2, $ 2, In this domain of net benefit, the average values are again all negative. Higher average net benefit is apparent for parenting women in gender-sensitive treatment levels 2, 3, and 4. Table 16. Average Net Benefit Values for Criminal Justice Services Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low $ 4, $ $20, $ 1, Medium Low -$ $ 2, $ $ 2, Medium High $ $ 1, $ 3, $ 2, High $ $ $ 3, $ 2, Criminal justice is the only outcome domain in which positive values were seen for average net benefit. No relationship between level of gender-sensitive treatment and criminal justice net benefit is apparent. Males without children appear to have the most negative criminal justice net benefit values. 57

71 APPENDIX J EXPLORATORY ANALYSES OF SPECIFIC NET BENEFITS Kruskal-Wallis ANOVA analyses found significant differences between levels of gendersensitive treatment for each specific net benefit domain. Table 17. Results from Kruskal-Wallis ANOVA Tests for Net Benefits by Gender-Sensitive Treatment Level Net Benefit Domain n df X 2 Sig.(p) 2 Healthcare Utilization 14, Criminal Justice 14, Economic Assistance 14, Earned Income 14, Pairwise comparisons with Bonferroni correction determined that healthcare utilization net benefit for treatment Level 4 was significantly higher than all other groups (p <.001). Treatment Level 4 also showed significantly higher (p <.001) earned income net benefit than other groups. For criminal justice, individuals in Levels 1 and 3 had significantly more negative net benefit than those in Levels 2 and 4 (p <.001). Finally, pairwise comparisons revealed that treatment Level 4 had significantly higher economic assistance net benefit compared to Levels 1 or 2 (p <.001), and treatment Level 3 had higher net benefit than Level 1 (p <.01). 58

72 APPENDIX K DESCRIPTIVE AND EXPLORATORY ANALYSES OF TOTAL NET BENEFIT Across all levels of treatment, there is evident variability in participants total net benefits as seen in the table below. The mean values of total net benefit appear to improve in the higher levels of gender-sensitive treatment and are best for individuals in Level 4. Table 18. Average Total Net Benefit by Gender-Sensitive Treatment Level Treatment Level n Mean Minimum Maximum SD 1. Low 4,098 -$9, $13,868, $2,760, $252, Medium Low 5,236 -$2, $ 5,246, $2,780, $120, Medium High 2,351 -$2, $ 2,499, $ 372, $ 72, High 2,910 -$1, $ 2,632, $2,770, $119, A Kruskal-Wallis ANOVA revealed that this difference between groups is significant, X 2 (3, n = 14,595) = 25.66, p <.001, 2 =.002. Pairwise comparisons indicated that the only significant differences in the net benefit are between treatment level 1 and all of the more gender-sensitive levels of treatment (p <.01). The following illustration displays the mean total, net benefit for each of the subgroups that are a focus of the study. 59

73 Level of Parent Nonparent Gender-Sensitive Treatment Male Female Male Female 1. Low $ 2, $ 2, $24, $ (n = 680) (n = 357) (n = 1,210) (n = 280) 2. Medium Low -$ 4, $ $ 3, $ 7, (n = 1,351) (n = 926) (n = 1,684) (n = 677) 3. Medium High -$ $ 1, $ 6, $ 1, (n = 827) (n = 312) (n = 951) (n = 250) 4. High -$ 2, $ 1, $ 6, $ 2, (n = 235) (n = 954) (n = 217) (n = 376) Figure 18. Mean Net Benefit for Parents and Nonparents by Gender Across Levels of Gender- Sensitive Treatment. Examination of this matrix reveals that the data do not precisely follow the pattern predicted, particularly for nonparents. It is seen that parents in the sample appear to accrue less of a deficit over the study period compared to nonparents. Women with children, in particular, follow this pattern although those in Level 3 of treatment were the only group who demonstrated positive net benefit. 60

74 APPENDIX L IMPACT OF THE RECESSION ON TOTAL NET BENEFIT A Mann-Whitney U analysis was performed to explore the impact of the financial crisis on overall benefits. This test indicated that benefits were significantly less negative for those who entered treatment before September 2008 than for individuals in treatment after that time, U (14,595) = 20,300,189.0, z = -9.36, p <.001, r =.09. The difference in mean, net benefits across cohorts of the study can be seen in Figure 1 below. Figure 19. Bar Graph Representing the Mean, Total Net Benefit Across Treatment Cohorts, with Confidence Interval Error Bars. In this bar graph, individuals in the cohorts affected by the economic recession (2008 and 2009) clearly exhibit more negative net benefit compared to groups preceding them.. 61

75 APPENDIX M FACTORIAL ANOVA EXAMINING RELATIONSHIP OF GENDER, PARENTING STATUS, AND GENDER-SENSITIVE TREATMENT ON TOTAL NET BENEFIT The graphs below display the relationship between total net benefit, gender-sensitive treatment level, parenting status, and gender, with males represented in the first graph and females in the second. Figure 20. Bar Graphs to Explore the Impact of Gender, Parenting Status, and Gender-Sensitive Treatment on Overall Net Benefit, with Confidence Interval Bars. Visually, it appears that parents of both genders who received higher levels of gendersensitive treatment accrue less negative total net benefit. However, the factorial ANOVA failed to reveal a significant three-way interaction between gender-sensitive treatment level, gender, and parenting status, F(3, 11,286) = 1.36, p =.158. Likewise, simple two-way interactions were not found to be significant, as seen in Table 19 below. Table 19. Summary of Simple Two-Way Interaction Findings for Total Net Benefit Interaction Label n df F Sig. (p) Gender by Parenting Status 11, Gender by Gender-Sensitive Level 11, Parenting Status by Gender-Sensitive Level 11,