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Kurtyka, Donald E.
The effects of a structured adherence intervention to HAART on adherence and treatment response outcomes
h [electronic resource] /
by Donald E. Kurtyka.
[Tampa, Fla] :
b University of South Florida,
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Dissertation (Ph.D.)--University of South Florida, 2008.
Includes bibliographical references.
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ABSTRACT: Background: Adherence to antiretroviral (ARV) medications in excess of 90-95% is necessary for optimal response to suppress HIV replication and to maintain and/or restore immune function. A number of interventions have been shown to improve ARV adherence, but no research has been conducted which evaluates proactive monitoring of pharmacy refill adherence and subsequent intervention when inadequate adherence is identified. Purpose: The purpose of this project was to compare treatment response, pharmacy refill adherence and self-reported medication adherence between two groups of patients: those participating in an AIDS Drug Assistance Program (ADAP) and those participating in a Medicaid-funded medication access program. The ADAP served as a structured adherence intervention (SAI) based on procedural and administrative processes required by the state-managed programAdditionally, covariates that can impact adherence were studied including utilization of adherence services and interventions and factors related to HIV disease, antiretroviral agents and sociodemographic factors. Method: This retrospective comparative study examined secondary data to assess 424 patients who received clinical and pharmacy services at one treatment site in 2005. Analysis: Logistic regression was performed to test the effects of the SAI on treatment response (CD4 and HIV RNA response), self-reported adherence, and pharmacy refill adherence while controlling for the covariates. Results: Patients participating in the SAI demonstrated higher levels of both self-reported and pharmacy refill adherence compared to patients receiving usual care.Although patients participating in the SAI program demonstrated better virologic (HIV RNA) responses to HAART compared to patients receiving usual care, immunologic (CD4 lymphocyte) responses to HAART were not significantly different compared to subjects in the usual care program. Conclusion/Discussion: This study provides information on the effects of a structured programmatic intervention on medication adherence and response to treatment and will be used to inform policy decision making at the local and State level.
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t USF Electronic Theses and Dissertations.
The Effects of a Structured Adherence Intervention to HAART on Adherence and Treatment Response Outcomes by Donald E. Kurtyka A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy College of Nursing University of South Florida Major Professor: Gail Powell-Cope, Ph.D. Lois O. Gonzalez, Ph.D. Stephen Luther, Ph.D. Mary Webb, Ph.D. Date of Approval: November 30, 2007 Keywords: HIV/AIDS, compliance, medication, antiretroviral therapy Copyright 2008, Donald E. Kurtyka
Acknowledgments I thank the members of my doctoral supe rvisory committee, colleagues, friends and family for their expertise, guidance a nd support during the development of this dissertation. I also thank the American Academy of Nurse Practitioners Foundation for awarding a doctoral student res earch grant to support the res earch activities associated with this dissertation and the Association of Nurses in AIDS Care for awarding a doctoral fellowship for my doctoral studies.
i Table of Contents List of Tables v List of Figures vii List of Acronyms viii Abstract x Chapter One: Introduction 1 Background 1 Adherence 2 Highly Active Antiretroviral Therapy (HAART) 2 Effect of HAART on Outcomes 3 Difficulty Adhering to HAART/Nonadherence 5 Overview and Implications 5 Prevalence of Nonadherence 6 Impact of Nonadherence 7 Factors Associated with Nonadherence 8 Patient Factors 8 Psychosocial Factors 9 Treatment Related Factors 10 Disease Characteristics 12 Patient-Provider Relationship and Social Support 13 Environmental Factors: The Healthcare System 14 Conclusions 15 Statement of the Problem 16 Purpose of The Study 17 Specific Study Aims and Research Questions 17 Hypotheses 18 Significance of the Study 18 Summary 19 Chapter Two: Review of the Literature 20 Introduction 20 Definition of Adherence 20 Measurement of Adherence 21 Introduction 21 Subjective Measures of Adherence 22 Patient Self-Report 22
ii Computer-Assisted Self-Interview 26 Visual Analog Scale 27 Self-Report Instruments and Questionnaires 28 Clinician Assessment 32 Objective Measures of Adherence 32 Directly Observed Therapy 33 Therapeutic Drug Monitoring 33 Biomedical Examination 34 Pill Counts 35 Electronic Monitoring Devices 35 Pharmacy Refill Monitoring 36 Combined Methods of Adherence Measurement 38 Measurement of Treatment Outcomes 40 Measurement of Virologic Outcomes 40 Measurement of Immunologic Outcomes 41 Measurement of Clinical Treatment Outcomes 42 Impact of Medication Adheren ce on Immuhnologic and Virologic Outcomes 43 Adherence Interventions: Review of Studies 43 Introduction 43 Patient Education and Counseling Interventions 44 Directly Observed Therapy 47 Adherence Devices and Reminders 48 Qualitative Reviews and Meta-Analyses of HAART Interventions 50 Summary 55 Chapter Three: Methods 57 Introduction 57 Treatment Conditions 57 Overview of the Study 57 Structured Adherence Intervention 59 Introduction: National AIDS Drug Assistance Program 59 Florida AIDS Drug Assistance Program 59 Usual Care 64 Medication Adherence Assessment 64 Adherence Services and Interventions 65 Summary 67 Research Design 67 Study Design 67 Study Population 68 Inclusion Criteria 68 Exclusion Criteria 68 Sample Size 69 Power Analysis 69 Setting 70 Study Variables 71
iii Dependent Variables 71 Self-Reported Medication Adherence 71 Pharmacy Refill Adherence 71 CD4 Lymphocyte Response 72 HIV RNA Response 72 Independent Variables: Treatment Condition 72 Independent Variables: Covariates 73 Adherence Services and Interventions 73 ARV Specific Factors 73 Sociodemographic Factors 73 HIV Disease Specific Factors 73 Data Sources 76 Data Collection Procedures 77 Procedures 77 Institutional Review Boards 77 Letter of Support 78 Data Management 78 Missing Data 78 Data Analysis Plan 78 Summary 79 Chapter Four: Findings 81 Study Sample 81 Subjects Excluded from Analysis 81 Characteristics of the Study Sample 85 Health and Income Related Characteristics 87 Substance Abuse and Mental Health Disorders 87 ARV Therapy Characteristics 89 Adherence Services and Interventions 90 Self-Reported and Pharmacy Refill Adherence 92 Treatment Response 93 Summary 93 Bivariate Analyses and Logistic Regression 94 Study Aim One: Adherence Outcomes 95 Self-Reported Adherence 95 Pharmacy Refill Adherence 100 Study Aim One: Summary 105 Study Aim Two: Treatment Response Outcomes 105 CD4 Lymphocyte Response 105 HIV RNA Response 109 Study Aim Two: Summary 113 Summary 114 Chapter 5: Discussion, Conclu sions and Recommendations 115 Introduction 115 Summary of the Study 115
iv Discussion and Conclusions 116 Study Aim 1 116 Study Aim 2 119 Limitations of the Study 120 Significance 124 Implications for Nursing Practice 127 Recommendations for Future Study 128 Summary 131 References 133 Appendices 157 Appendix A: Institutional Revi ew Board Exemption: USF 158 Appendix B: Institutional Review Board Exemption: DOH 160 About the Author End Page
v List of Tables Table 1 FDA Approved Antiretroviral Agents 3 Table 2 Adherence Measurement Instruments 29 Table 3 Quantitative Plasma HIV RNA Techniques 41 Table 4 Adherence Management Gu idelines and Recommendations 55 Table 5 Adherence-Related Statemen ts in the Florida ADAP Program Manual 61 Table 6 Florida ADAP Procedures Re lated to Nonadherence and Failure to Pick-Up 62 Table 7 Medication Adherence Asse ssment Questions (Self-Report) 65 Table 8 Services and Interventions Provided by the Adherence Specialist 66 Table 9 Power Estimates Based on Projected Sample Population 70 Table 10 Variables, Definitions and Measurement: Part I 74 Table 11 Variables, Definitions and Measurement: Part II 75 Table 12 Subjects Excluded from Study 82 Table 13 Comparison of Sociodemogr aphic Characteristics of SAI Group, Usual Care Group and Subjects No t Meeting Inclusion Criteria 83 Table 14 HIV Disease and Comorbid Conditions Characteristics of SAI Group, Usual Care Group and Subj ects Not Meeting Inclusion Criteria 84 Table 15 Sociodemographic Composition of the Study Groups 86 Table 16 Health and Income Relate d Characteristics of the Study Group 88 Table 17 Substance Abuse and Mental Health Disorders in Study Group 89
vi Table 18 Use of Antiretrovira l Medications in Study Groups 90 Table 19 Adherence Services and Intervention 91 Table 20 Self-Reported Adherence and Pharmacy Refill Adherence 92 Table 21 Treatment Respons e by Group Membership 93 Table 22 Bivariate Analysis Self Reported Adherence: Sociodemographic and HIV Disease Specific Factors 96 Table 23 Bivariate AnalysisSe lf-Reported Adherence: ARV and Adherence Counseling and In tervention Specific Factor s 98 Table 24 Logistic Regression Analysis : Summary of Predictors of SelfReported Adherence 100 Table 25 Bivariate AnalysisPharmacy Refill Adherence: Sociodemographic and HIV Disease Specific Factors 101 Table 26 Bivariate AnalysisPharmacy Refill Adherence: ARV and Adherence Counseling and Inte rvention Specific Factors 103 Table 27 Logistic Regression Analysis: Summary of Predictors of Pharmacy Refill Adherence 104 Table 28 Bivariate Analysis CD4 Ly mphocyte Response: Sociodemographic and HIV Disease Specific Factors 106 Table 29 Bivariate AnalysisCD 4 Lymphocyte Response: ARV and Adherence Counseling and Inte rvention Specific Factors 107 Table 30 Logistic Regression Analysis: Su mmary of Predictors of CD4 Lymphocyte Response 109 Table 31 Bivariate AnalysisHIV RNA Response: Sociodemographic and HIV Disease Specific Factors 110 Table 32 Bivariate AnalysisHIV RNA Response: ARV and Adherence Counseling and Interven tion Specific Factors 112 Table 33 Logistic Regression Analys is: Summary of Predictors of HIV RNA Response 113
vii List of Figures Figure 1 Conceptual Model for Evalua ting the Effects of a Structured Adherence Intervention to HAART on Adherence and Treatment Response Outcomes 58
viii List of Acronyms AACTG Adult AIDS Clinical Trial Group ADAP AIDS Drug Assistance Program AIDS Acquired Immunodeficiency Syndrome ARV Antiretroviral AZT Zidovudine CAS Composite Adherence Score CASI Computer-Assisted Self-Interview CPCRA Community Programs for Clinical Research on AIDS DHHS Department of Health and Human Services DOT Directly Observed Therapy EDM Electronic Data Monitoring EI Entry Inhibitor HAART Highly Active Antiretroviral Therapy HCA Health Center Administrator HIV Human Immunodeficiency Virus IRB Institutional Review Board MEMS Medication Event Monitoring System MSM Men Who Have Sex with Men NNRTI Non-Nucleoside Reverse Transcriptase Inhibitor NP Nurse Practitioner NRTI Nucleoside Reverse Transcriptase Inhibitor PCP Primary Care Provider
ix PI Protease Inhibitor PMS Pharmacy Management System RCT Randomized Controlled Trials RNA Ribonucleic Acid SAI Structured Adherence Intervention SMAQ Simplified Medication Adherence Questionnaire SPSS Statistical Package fo r the Social Sciences TDM Therapeutic Drug Monitoring VAS Visual Analogue Scale
x The Effects of a Structured Adherence Intervention to HAART on Adherence and Treatment Response Outcomes Donald E. Kurtyka ABSTRACT Background: Adherence to antiretroviral (ARV) medications in excess of 90-95% is necessary for optimal response to suppre ss HIV replication and to maintain and/or restore immune function. A number of in terventions have been shown to improve ARV adherence, but no research has been conducte d which evaluates proactive monitoring of pharmacy refill adherence and subsequent intervention when inadequate adherence is identified. Purpose: The purpose of this project was to compare treatment response, pharmacy refill adherence and self-reported medication adherence between two groups of patients: those participating in an AIDS Drug Assistance Program (ADAP) and those participating in a Medicaid-funded medication access program. The ADAP served as a structured adherence intervention (SAI) based on procedural and administrative processes required by the state-managed program. A dditionally, covariates that can impact adherence were studied includi ng utilization of adherence se rvices and interventions and factors related to HIV diseas e, antiretroviral agents a nd sociodemographic factors. Method: This retrospective comparativ e study examined secondary data to assess 424 patients who received clinical and pharmacy se rvices at one treatment site in 2005.
xi Analysis: Logistic regression was perfor med to test the effects of the SAI on treatment response (CD4 and HIV RNA re sponse), self-reported adherence, and pharmacy refill adherence while controlling for the covariates. Results: Patients participating in the SAI demonstrated higher levels of both selfreported and pharmacy refill adherence compared to patients receiving usual care. Although patients participating in the SAI pr ogram demonstrated better virologic (HIV RNA) responses to HAART compared to patients receiving usual care, immunologic (CD4 lymphocyte) responses to HAART were not significantly different compared to subjects in the usual care program. Conclusion/Discussion: This study provi des information on the effects of a structured programmatic intervention on medicat ion adherence and response to treatment and will be used to inform policy decision making at the local and State level.
1 CHAPTER ONE: INTRODUCTION Background Human immunodeficiency virus (HIV) in fection was originally considered a terminal illness when identified in the early 1980s. Nearly everyone who contracted the disease advanced to Acquired Immunodefici ency Syndrome (AIDS) and death (Bartlett & Gallant, 2005). Treatment with the firs t antiretroviral agent zidovudine (AZT), which became available in the late 1980s, gave shor t-term encouragement to those with HIV disease. Within a year, however, most pe rsons no longer responded to this medication and became ill or died. A breakthrough occurr ed in 1996 with the introduction of an effective combination thera py capable of suppressing HIV replication. These potent combination drug regimens now known as highl y active antiretrovir al therapy (HAART) redefined HIV disease into a chronic illness requiring long-term management rather than a terminal disease (Johnson et al., 2006) During the last decade, advances in the scientific understand ing of HIV dynamics and pat hogenesis, the development and widespread use of quantitative HIV ribonucleic acid (RNA) assays to quantify serum levels of HIV, and the availability and use of powerful antiretroviral agents culminated in dramatic changes in HIV clinical care and improved clinical outcomes (Williams et al., 2006).
2 Adherence The World Health Organization (2003) broa dly defined adherence as the extent to which a patient's behavior, such as taking prescribed medications or following a diet, corresponds with the interventi ons of the healthcare provide r. Medication adherence in HIV disease has been defined as the ability of the person living with HIV/AIDS to be involved in choosing, starting, managing a nd maintaining a combination medication regimen to control viral replication and im prove immune function (Jani, 2002). The terms adherence and nonadherence are meant to be nonjudgmental, st atements of fact rather than expressions of blame toward the patient or provider (B angsberg, Perry et al., 2001). Highly Active Antiretro viral Therapy (HAART) Antiretroviral (ARV) drugs for the treatment of HIV disease are br oadly classified by the phase of the HIV lifecycle that the dr ug inhibits. Antiretrovi ral drugs currently licensed for clinical use by the Food and Drug Administ ration are classified as nucleoside/nucleotide reverse transcriptas e inhibitors (NRTI), non-nucleoside reverse transcriptase inhibitors (NNRT I), protease inhibitors (PI), an d entry inhibitors (EI). Table 1 lists these agents by classifica tion and mechanism of action. HAART is the combination of at least three ARV drugs that target at least two different parts of the HIV lifecycle or stop the virus from enteri ng CD4 lymphocytes. A panel of experts convened by the Department of Health and Human Services regularly publishes guidelines suggesting preferred a nd alternative combinations that can be combined to form a HAART regimen. HAA RT typically includes two NRTIs paired with an NNRTI or a PI. In advanced st ages of HIV disease or when significant
3 medication resistance is present, HAART regime ns may include more than four or five agents (National Institutes of Health, 2006). Table 1 FDA Approved Antiretroviral Agents Classification Mechanism of Action Agents Nucleoside and nucleotide reverse transcriptase inhibitors (NRTI) The reverse transcription process is blocked. HIV RNA cannot be converted to HIV DNA and viral reproduction is terminated. Zidovudine Lamivudine Stavudine Entricitabine Didanosine Tenofovir Abacavir Non-nucleoside reverse transcriptase inhibitors (NNRTI) The reverse transcription process is blocked. HIV RNA cannot be converted to HIV DNA and viral reproduction is terminated. Nevirapine Efavirenz Delavirdine Protease inhibitors (PI) Final viral assembly is inhibited when protease enzymes are not availabl e to reassemble viral particles and produce new virus. Saquinavir Ritonavir Nelfinavir Indinavir Lopinavir/ritonavir Atazanavir Fosamprenavir Tipranavir Darunavir Entry Inhibitors The process of HIV binding to a CD4 lymphocyte is interrupted, thus blocking the ability of HIV to infect a CD4 lymphocyte. Enfuvirtide Maraviroc Effects of HAART on Outcomes The introduction of HAART has dramatica lly decreased morbidity and mortality among HIV-infected patients throughout the developed worl d (Egger et al., 1997; Hogg et al., 1998; Palella et al., 1998). In the United States, mortality from HIV infection decreased by 70% between 1996 and 1998 and decreased an additional 14% between 1998 and 2002 (Centers for Disease Control, 2004; Frick, Tapia, Grant, Novotny, &
4 Kerzee, 2006; Hogg et al., 1998; Palella et al., 1998). The incidence of opportunistic infections associated with AIDS has also decreased significantly w ith the use of HAART (Grabar et al., 2000). Patients ability to adhere to complex regimens is an essential component of successful antiretroviral therapy (Kitahata et al., 2004) and is wi dely regarded as the most important mutable determinant of clinical outcomes in the HIV-infected patient (Wood et al., 2003). Although a decrease in the numbe r of CD4 lymphocytes is the strongest predictor of progression to AIDS and deat h, adherence to HAART is the second most common predictor (Bangsberg, Perry et al., 2001 ; Garcia de Olalla et al., 2002; Hogg et al., 2002; Machtinger & Bangsberg, 2006; Wood et al., 2004). In studying levels of adherence, Bangsberg and his team (2001) found that no patients in a group of highly adherent patients developed AIDS-defini ng events over the 16 months of the study compared to those with moderate and low adherence. Each 10% difference in mean adherence was found to be associated with a 28% reduction in risk of progression to AIDS. Another group concluded that adhe rence behavior is a dynamic process and continued adherence was associ ated with improved response to ARV therapy (Carrieri et al., 2001). It has been estimated that a nonadherent patient receiving HAART is 3.87 times more likely to die than an adherent pati ent on the same therapy (Garcia de Olalla et al., 2002). Although adherence to HAART at a level a bove 95% has been associated with optimal viral suppression, the re lationship between various le vels of adherence, resulting virologic treatment responses, and long-term cl inical outcomes has not been determined. Previous studies have examined relatively sm all numbers of patients in relation to short-
5 term virologic response to HAART. Adhe rence to antiretroviral therapy in both the short-term and the long-term is crucial for treatment success and must be continually reinforced (Hammer et al., 2006). Difficulty Adhering to HAART / Nonadherence Overview and Implications The Department of Health and Human Se rvices (DHHS) develops and publishes HIV treatment guidelines on a regular basis. These guidelines reinforce that one of the most important issues in managing patient s receiving HAART is a dherence to therapy (National Institutes of Health, 2006). When treating HIV disease, adherence levels need to be at the 90-95% level to achieve and maintain therapeutic effectiveness (Murphy, Lu, Martin, Hoffman, & Marelich, 2002; Paterson et al., 2000). Maintaining this threshold can be complex and difficult. Adherence to HAART has been described as the AchillesHeel of antiretroviral thera py (Simoni, Frick, Pantalone, & Turner, 2003) because of the difficulty associated with maintaining such high levels of medication adherence. There are a number of contributing fact ors that make 100% adherence to HAART difficult for many patients including the comp lexity of the HAART regimen (multiple pills, multiple doses, food requirements and rest rictions), immediate and long-term side effects associated with the ARV agents, and comorbid conditions such as active substance abuse and mental illness.
6 Prevalence of Nonadherence Nonadherence to medication therapy has b een a problem for as long as remedies for health conditions have been prescribed (Chesney, 2006). Evidence shows that poor adherence to ARV regimens has serious consequences for HIV-infected patients including failure to prevent viral replication, an increased likelihood of developing viral resistance, decreasing CD4 lymphocyte c ounts, ineffective disease tr eatment, increasing illness, advancement to AIDS, and ultimately death (Bangsberg, Hecht et al., 2001; Gifford et al., 2000; Miller & Hays, 2000a; Murphy, Lu, Ma rtin, Hoffman, & Marelich, 2002; Turner, 2002). Despite these risks, nonadherence to HAART is widespread in the United States and in Europe with estimates of the percen tage of prescribed doses taken ranging from 60% to 70% (Bangsberg et al., 2000; Bar tlett, 2002; Gordill o, del Amo, Soriano, & Gonzalez-Lahoz, 1999; Martin-Fernandez, Escobar-Rodriguez, Campo-Angora, & Rubio-Garcia, 2001; Moatti et al., 2000; Nieuwkerk et al., 2001). The average rate of adherence varies by the method used to assess it and the group studied. In one prospective study, 140 i ndividuals in a public U.S. hospital HIV clinic were followed for one year after initi ation of HAART. The i nvestigators assessed adherence using three methods and calculated a composite adheren ce rate of only 71%. Only six percent of the patients took at leas t 95% of their medicati ons, the optimal level for durable virologic and clini cal success (Golin et al., 2002). Studies of different groups of HIV-positive individuals in the United Stat es and abroad generally show similar, suboptimal rates of adherence (Gordillo, del Amo, Soriano, & Gonzalez-Lahoz, 1999; Knobel et al., 2001; Murri et al., 2000; Wa lsh, Mandalia, & Gazzard, 2002). Rates of adherence are known to decline over time. Most patients taking HAART, regardless of
7 their background or life situati on, will encounter difficulties with adherence at some point (Howard et al., 2002; Mannheimer, Frie dland, Matts, Child, & Chesney, 2002). Inadequate adherence may eventually undermine the dramatic improvements in HIV-related health parameters seen in resour ce-rich countries and the expected response in developing countries as HAART become s more widely available. Not only can nonadherence negatively impact clinical outcome s, it can add significan tly to the cost of care. It was, however, the recognition that nonadherence results in tr ansmittable forms of drug resistant strains of HIV th at brought attention to the pr oblem rather than suboptimal clinical outcomes (Chesney, 2006). Although most experts accept that adhere nce to antiretroviral medication is critical to the effectivene ss of HIV treatment (Bangsberg et al., 2000; Haubrich et al., 1999; Liu et al., 2001), few rigorously designed studies have documented the efficacy of interventions to improve adherence to ARV treatment (Williams et al., 2006). While the potency of current therapeutic options fo r treatment of HIV disease has decreased morbidity and increased survival, imperfect adherence to HAART remains a major cause of treatment failure among patients with HIV disease (McNabb et al., 2001) (Palella et al., 1998; Paterson, Potoski, & Capitano, 2002). Impact of Adherence Highly active antiretroviral therapy has resu lted in a longer life during the chronic stage of HIV infection. The Swiss HIV c ohort study initially documented this trend, showing an increase in the survival rate fr om 19% in 1991 to 62% in 1996 (Egger et al., 1997). Recent projections published in 2006 estimated that the life expectancy of someone currently beginning care for treatm ent of HIV is at an all-time high of 24.2
8 years (Schackman et al., 2006) It is imperative that research be conducted to more fully understand adherence and to iden tify evidence-based interventions that can be developed and implemented to improve patient outcome s in people living with HIV disease. Factors Associated with Nonadherence Adherence to medication is a complex behavior which is influenced by many factors related to the patient, the prescribed treatment, the disease state, the healthcare provider and patient-healthcare provider re lationship, and the healthcare system. Many studies have yielded discor dant results, making it difficult to achieve consensus on modifiable barriers and predictors on which adherence intervention strategies should be designed (Ammassari et al., 2002). Some of these factors are immutable such as age, income, literacy, and the patient's social milieu while other factors are potentially alterable, such as depressi on, substance abuse, regimen complexity, medication side effects, and the therapeutic relations hip between patient and provider. Patient Factors Patient factors affecting a dherence include the sociodem ographic factors of age, gender, race/ethnicity, income, education, l iteracy, housing status, insurance status, and risk factor for acquisition of HIV infecti on. Psychosocial factor s typically encompass mental health issues, substance use, soci al climate and support, knowledge and attitudes about HIV and its treatment). Additiona lly, patients have identified many diverse reasons for missing their medications. Gifford and his colleagues (2000) found that organizational difficulties (e.g., too busy, forgot, away from home, change in routine) and emotional issues were the most common reasons for missed doses.
9 Conflicting results have been reported regarding the association between sociodemographic factors and adherence beha vior. When an association was found, the direction was consistent: younger age, non-wh ite race/ethnicity, lower income, lower literacy, and unstable housing we re negatively associated with adherence in resource-rich settings. Gender, educational level, insurance status, and HIV risk f actors generally were not associated with adherence behavior (Alt ice, Mostashari, & Fr iedland, 2001; Gifford et al., 2000; Golin, Isasi, B ontempi, & Eng, 2002; Haubrich et al., 1999; Holzemer et al., 1999; Kleeberger et al., 2001; Mannheimer, Friedland, Matts Child, & Chesney, 2002; Paterson et al., 2000). Psychosocial Factors More consistent associati ons were found between certain psychosocial factors and adherence behavior. Common predictors of less than adequate adherence include untreated depression, other psychiatric morbid ity such as anxiety and bipolar disease, stressful life events and lack of social and family support (Ammassari et al., 2001; Cinti, 2000; Gordillo, del Amo, Soriano, & Gon zalez-Lahoz, 1999; Holzemer et al., 1999). Active substance abuse including cocaine, marijuana, amphetamines, sedatives, and moderate to heavy alcohol consumption have also been inversely linked to adherence (Golin et al., 2002). Patients who are unable to correctly identify their drug regimen or describe the relationship between adherence a nd drug resistance are al so more likely to be nonadherent. Belief in the efficacy of the medication and the presence of social support systems has been positively related to adherence to HIV (Amm assari et al., 2002) (Miller et al., 2003; Stone et al., 2001; Tucker, Burnam, Sherbourne, Kung, & Gifford, 2003).
10 Treatment Related Factors Factors related to the treatment regime n can impact adherence including the number of pills prescribed, complexity of the regimen including dosing frequency and food instructions and restrictions, convenie nce of the regimen, type of ARV agents prescribed (e.g. protease inhi bitor vs. non-nucleoside reverse transcriptase-based), side effects associated with the agents and the ability to incorporate the regimen into an individual's daily routine (Bartlett, 2002; Chesney, 2000; Walsh, Mandalia, & Gazzard, 2002). In general, adherence declines with th e emergence of side e ffects. Side effects associated with HAART are common and incl ude transient events such as diarrhea, nausea and fatigue as well as longer-lasting adverse effects such as metabolic disorders including diabetes and lipi d disorders, lipodystrophy and neuropathy (Chesney, 2003). With regards to HAART, side effects are th e primary cause of nonadherence and account for more regimen changes than do treatm ent failures (Ammassari et al., 2001). The association between the number of dos es per day and patient adherence is well described, with adherence declining as dosing frequency increases (Bartlett, DeMasi, Quinn, Moxham, & Rousseau, 2001; Claxton, Cramer, & Pierce, 2001). High pill burden has been reported as a prim ary reason for missing or discontinuing HAART (Bartlett, DeMasi, Quinn, Moxham, & Rouss eau, 2001; Trotta et al., 2002). With the continued development of newer antiretrovira l treatment agents, at tention has focused on improving the efficacy, convenience and tolera bility of medications with particular emphasis placed on reducing pill burden and dos ing frequency. Data are emerging that demonstrate a positive association on a dherence with once daily HAART regimens (Johnson et al., 2006).
11 Bartlett (2001) identified pill burden as the most significant predictor of HAART response. Since that time, treatment regimens have continually been simplified. In 2006, a one-tablet, once-daily HAART regimen became availabl e and greatly reduced the scheduling requirements and pill burden a ssociated with HAART (U.S. Food and Drug Administration, 2006). In a meta-analysis of 23 clinical trials i nvolving 3,257 patients to determine predictors of virologic suppressi on, researchers found pill burden to be the most significant predictor of antiretroviral response (Stone et al., 2001). Although oncedaily regimens demonstrated improved attainme nt of virologic control in two large RCTs that compared once-daily with twice-daily regime ns, it is not clear if the benefit seen with once-daily HAART resulted from increased pot ency of the regimens studied, better adherence, or both (Molina, Ferchal, & Rancinan, 2003) (Raffi, Saag, & Cahn, 2003; Saag, Cahn, & Raffi, 2002). Data from additi onal studies related to this issue are expected in the near future. Stone et al. (2001) conducted a cross-s ectional survey of women and found that self-reported adhere nce was better among patients with less complex HAART regimens due in part to the fact that patients understanding of regimen dosing decreases as regimen complexity increases. Therefore, simplifying HAART regimens may have an important role in improving patients adherence. Convers ely, increasing complexity in the medication regimen is associated with decreasing patient adherence.
12 Disease Characteristics Disease characteristics affecting adheren ce include the stage and duration of HIV infection, HIV-related symptoms and AIDS -associated opportunistic infections. Adherence rates are consistently lower fo r a long term, chronic illnesses and for asymptomatic conditions (Graney, Bunting, & Russell, 2003). Over time, even the most motivated patients may find it increasingly difficu lt to remain adherent (Ickovics et al., 2002). Asymptomatic HIV-infected patients ma y be less adherent since the only immediate perceived effect of HAART may be deterioration in health status and wellbeing as a result of medication side effects and disruptions in daily routine. Certain patterns of behavior in patients with chroni c, asymptomatic illness have been linked to nonadherence including not filling prescriptions forgetting doses, taking incorrect doses, stopping medication too soon, and self-regulatin g the regimen to manage side effects (Ammassari et al., 2002; Chesney, 2003; Hubba rd, 2006; Trotta et al., 2002). Conversely, Williams (1999) found that adhere nce is frequently greater in patients with advanced HIV disease as the improveme nt in disease-related symptoms resulting from controlling viral replic ation with HAART often outweig hs the adverse effects of treatment. Increased adherence was seen in patients with opportunistic infections. The researchers believed this was explained by the patients desire for improved health and a stronger motivation to adhere. Several st udies described a rela tionship between HIVrelated symptoms and nonadherence (H olzemer et al., 1999; Wagner, 2002).
13 Holzemer and colleagues (1999) found that clients with higher symptom scores, particularly depression, were more likely to be nonadherent to medication, not to follow provider advice, and to miss appointments while those who reported having a meaningful life, feeling comfortable and well cared for, using their time wisely, and taking time for important things were both more adherent to their medications and more likely to follow provider's advice. They suggested that stra tegies to enhance adherence should include recognition and treatment of symptoms (particul arly depression) and an understanding of clients' perceptions of their environment. Patient-Provider Relati onship and Social Support Several studies have documented that posit ive relationships with friends, family and healthcare providers can impact adhere nce to medication. Re searchers found that a positive patient-provider relationship can be an important motivating factor for taking and adhering to HAART. Factors that have been identified as strengthening patient-provider relationships include communication quality and clarity, compassion, willingness to include patients in treatment decisions, adequacy of referrals, and convenience of visiting the provider. Conversely, frustration for provi ders has been associated with lack of adherence to treatment, missed appointments, complexity of treatment regimens and medication side effects (Ammassari et al., 2002; Chesney, 2000). Health beliefs, coping skills and rapport with healthcare providers have been correlated with adherence to HAART. Patients are more likely to be adherent if they have confidence in, and guidance from, thei r healthcare providers (Bertholon, Rossert, & Korsia, 1999; Holstad, Pace, De, & Ura, 2006) The patient's overall satisfaction and
14 trust in the provider and clin ical staff along with the pati ent's opinion of the provider's competence, willingness to include the pati ent in the decision-making processes, the adequacy of referrals and the convenience of visiting the provider can affect adherence (Ammassari et al., 2002; Chesne y, 2000; Stone et al., 1998). Findings from two studies suggested an a ssociation between stressful life events and nonadherence (Gifford et al ., 2000; Moatti et al., 2000). Lack of social or family support and poor self-efficacy have also been found to be an important risk factor for nonadherence (Altice, Mostashari, & Friedl and, 2001; Catz, Kelly, Bogart, Benotsch, & McAuliffe, 2000; Gordillo, del Amo, Sori ano, & Gonzalez-Lahoz, 1999; Murri et al., 2002; Stone et al., 1998). Ammassari and coll eagues (2002) concluded that the presence of tangible and emotional support can redu ce barriers and increase motivation for adherence. Lucas et al. (2004) found that patients w ho kept medical appointments were more likely to be adherent to medication. Additi onally these researchers found that adherence was associated with patients understanding th at suboptimal adherenc e leads to resistance and a recognition that taking all medication doses is critically associated with adherence. It has been suggested that members of the healthcare team work in partnership with patients and to involve representatives from the entire HIV community to strengthen collaborative efforts related to the pr omotion of adherence (Chesney, 2000). Environmental Factors: The Healthcare System Research addressing the relationship betw een the healthcare setting and adherence behavior are limited. Chesney (2000) found that aspects of the c linical setting can
15 positively impact adherence such as easy access to ongoing primary care and convenience in scheduling appointments, invol vement in a dedicated adherence program, availability of transporta tion and child care services, comfort with the clinical environment, perceived confidentiality, and sa tisfaction with past experiences in the healthcare system. Conversely, dissatisfacti on with experience in th e healthcare system has been associated with nonadherence. Wo men studied by Powell-Cope and colleagues (2003) identified difficulty obtaining medication refills and concerns related to confidentiality as barriers to adherence. Conclusions Adherence to highly active antiretroviral therapy is strongly correlated with both immunologic and virologic succ ess, as evidenced by decr eased HIV RNA levels and increased CD4 lymphocyte counts. Unfortunate ly, achieving and maintaining high levels of adherence to complex HAART regimens can be very difficult (Ba ngsberg et al., 2000; Haubrich et al., 1999). The reasons for inadeq uate adherence are complicated and often involve many variables related to the me dications, co-morbid health conditions, environmental barriers and psychosocial con cerns. Consequently, successful HAART is often limited due to inadequate medication adherence (Carpenter 1997; Knobel et al., 2001). Experts in the field of HIV have come to consensus on the importance of adequate adherence to HAART (Bangsberg & Deek s, 2002) (Chesney, 2003; Paterson et al., 2000). Despite the importance of this s ubject, empiric research on adherence interventions for HIV-infected individuals is minimal. Simoni and colleagues (2003) described adherence research as being in th e embryonic stage. In their 2004 adherence
16 supplement, the DHHS guidelines concluded th at interventions to improve adherence for HAART are insufficiently characterized and understood, and additional research regarding the topic needed (National Instit utes of Health, 2004). Berg and Arnsten stressed that adherence measurement is need ed in clinical and research settings, and called for research to evaluate methods a nd provide recommendations for research and clinical care (Berg & Arnsten, 2006). The importance of adherence is demonstr ated by the integration of adherence recommendations into national consensus guide lines for the use of HAART antiretroviral therapy (National Institutes of Health, 2006). While more adherence research is called for, methodological barriers are ev ident. Uncertainty exists regarding the best measure of adherence in both clinical practice and in rese arch settings. Patient report, pill counts, and provider estimates may overestimate adhere nce, while electronic methods such as the Medication Event Monitoring System (M EMS) frequently underestimate ARV adherence, are too costly, and are not pract ical for use in routine clinical care. Furthermore, the process of monitoring patie nts behavior when m easuring adherence may act as an intervention that change s adherence (Turner & Hecht, 2001). Statement of the Problem Adherence to HAART has arisen as one of the most important issues in the effective treatment of HIV disease. The difference in long-term viral suppressive response between those who take their medici ne correctly 90-95% of the time and those who do not, can be the difference between life and death (Pater son et al., 2000). The identification of effective and clinically pr actical adherence interv entions could greatly
17 improve the response to treatment modalities. Fo r this reason, it is critical to determine interventions that promote adherence Purpose of the Study The purpose of this retrospective compara tive study was to evaluate the effects of a structured adherence interv ention (SAI) as a co mponent of an existing antiretroviral access program on adherence to HAART and response to treatment as compared to usual care. In the structured adherence interven tion providers closely monitored monthly HIV medication refills and provided structured adherence intervention when indicated. Patients receiving usual care were enro lled in a Medicaid-funded medication access program and did not receive ongoing medica tion refill monitoring and structured adherence intervention. Both patient groups received their ARV medications and outpatient HIV medical care from a single treatment center and pharmacy. Specific Study Aims and Research Questions Study Aim 1: To determine whether patie nts participating in the SAI program experienced higher levels of adherence comp ared to patients receiving usual care, controlling for adherence servi ces and intervention, HIV disease-specific factors, ARVspecific factors, and sociodemographic factors. Research questions : 1a. Is there a difference in self-reported adherence in subjects participating in the SAI program compared to those who receive usual care? 1b. Is there a difference in pharmacy ref ill adherence in subjects participating in the SAI program compared to those who receive usual care?
18 Study Aim 2: To determine whether patie nts participating in the SAI program experienced improved response to treatment co mpared to patients receiving usual care, controlling for HIV disease-spec ific factors, ARV-specific f actors, and sociodemographic factors. Research questions: 2a. Is there a difference in CD4 lymphocyt e response in subjects participating in the SAI program compared to those who receive usual care? 2b. Is there a difference in HIV RNA res ponse in subjects participating in the SAI program compared to t hose who receive usual care? Hypotheses 1. Patients participating in the SAI will have higher levels of self-reported and pharmacy refill adherence compared to pa tients receiving usual care, controlling for covariates. 2. Patients participating in the SAI progr am will have better immunologic (CD4 lymphocyte) and virologic (HIV RNA) responses to HAART compared to those receiving usual care, controlling for covariates. Significance of the Study Although a great deal of progress has been made in the measurement of medication adherence in HIV disease and evaluation of adherence interventions, additional work is still needed. Ongoing re search is needed to develop and validate accurate, practical and cost-effective methods for measuring adherence to HAART that can be used in both developing and indus trialized countries. Study samples should
19 include more racial and ethnic minorities a nd women to more accurately represent the current population with HIV disease. Despite the importance of adherence re lated to HAART, empiric research on adherence interventions for HIV-infected individuals is minimal. Although several researchers have studied se lf-reported and pharmacy refi ll adherence, there are no published studies of medication access progr ams that proactively monitor pharmacy refills and initiate adherence interventions wh en adherence deficiencies are identified. This study is designed to further the existing knowledge with relati on to these additional variables. The findings will provide information on the effects of a programmatic intervention on medication adherence and respon se to treatment that can be used to inform policy decision making at the lo cal, regional, and st ate levels. Summary This chapter presented the importance of adherence related to HAART and the need to identify effective interventions to fost er adherence. High le vels of adherence to HAART are necessary for optimal response to therapy. Optimal response to HAART results in improved viral suppression, im proved immunologic response and functioning, and ultimately a decrease in morbidity a nd mortality. Nonadherence remains strongly associated with mortality (Wood et al., 2003). Limited research st udies have identified effective interventions which can improve adherence.
20 CHAPTER TWO: REVIEW OF THE LITERATURE Introduction This chapter includes a review of res earch related to the measurement of adherence to HAART in HIV disease and interventions to increase or strengthen adherence to HAART. Although adherence to HAART is the st rongest predictor of HIV viral suppression, drug resistance, disease progression and death in HIV-infected individuals, there is no standard approach to adherence assessment and intervention in routine clinical practice. Definition of Adherence HAART adherence researchers have yet to identify a standardized definition of adherence and few studies use consistent m easures of adherence. Therefore adherence data must be interpreted w ith caution and comparison among studies is difficult (Hill, Kendall, & Fernandez, 2003; Powell-Cope Toney, & Montano, 2001). One study may define adherence as the percentage of pr escribed doses taken within two hours of scheduled dosing time over a 1-week period ac cording to electronic data monitoring (EDM) while another may operationalize adheren ce as the percentage of prescribed doses taken in the last month according to self-report Adherence studies are also inconsistent with regard to measurement of clinical outcomes. So me studies reported immunologic effects in terms of CD4 lymphocyte response wh ile others reported virologic outcomes in terms of HIV RNA response. Some studies reported both imm unologic and virologic
21 outcomes while others do not address either of these outcome measurements (Simoni, Pantalone, Frick, & Turner, 2005). Holzemer et al. (1999) even expanded the concept of adherence beyond only medication adherence to include following clinician instructions and missed appointments. Measurement of Adherence Adherence to HAART is a complex issue involving social, cultural, economic and personal factors. This complexity makes it di fficult to identify a reliable and valid single measure of adherence that is appropriate fo r all settings (Chesne y, 2006). Research and clinical care have also been hindered by the lack of an inexpensiv e, quick and accurate method to measure adherence (Dunbar-J acob & Mortimer-Stephens, 2001). Chesney (2006) argued that it may be impossi ble to develop a gol d standard definition of adherence and a standard measurement of adherence as the HIV epidemic is too diverse throughout the globe. Introduction Clinical studies employ a number of methods, alone or in combination, to measure medication adherence. A number of studies have shown, however, that the objective measures used in re search, although impractical for most clinical settings, are more sensitive than patient self-report fo r detecting medication adherence (Machtinger & Bangsberg, 2006). Adherence is usually measured as either a categorical or continuous variable. Two common approaches to defining a categor ical outcome are to consider whether the patient missed any pills over a specific interval (such as the last 3 or 7 days) or whether the patient has exceeded a set percentage of doses taken (usually 95%). This simplistic
22 dichotomous classification may not capture the complexity of adherence patterns such as adhering to timing of medication doses, medication-specific food requirements and taking the correct number of pills (Chesney, 2003). Measuring adherence as a continuous vari able is less common in the literature than a dichotomous measure. When measured as a continuous vari able, adherence is usually defined as the proportion of prescribed doses taken as measured by an electronic drug monitoring device, self-report or pill count. Adherence can also be measured as a continuous variable by obtaining the percenta ge of pills available for consumption by pharmacy refill records or the number of missed doses over a specified time period (Simoni, Pantalone, Frick, & Turner, 2005). Many published studies simply reported the criterion for adherence as meeting the mi nimum level of drug consumption such as greater than 70, 80, or 90% (Fogarty et al., 2002). Adherence measurement is frequently clas sified as subjectiv e (in the opinion of the patient) or objective (dat a recorded independ ently of the patient ) (Orrell, 2005). Subjective approaches include self-report and self-administe red questionnaires in which patients are asked to report the number of medications missed or taken based within a designated time frame. Objective methods include electronic da ta monitoring (EDM) devices, pill counts and pharmacy refill da ta. These various methods of measuring adherence will each be explored. Subjective Measures of Adherence Patient Self-Report Patient self-report offers a relatively in expensive, simple and non-intrusive means of incorporating adherence data into routine clini cal practice and the research setting.
23 Self-report is useful due to th e relatively low cost, ease of administration and flexibility of use in a variety of settings (Ba ngsberg, Bronstone, Chesney, & Hecht, 2002). Although self-report is one of the most common methods of assessing medication adherence, inaccuracy may result due to im precise or inconsistent questioning, patient forgetfulness and poor recall, or the patient s desire to provide socially desirable responses along with a desire to please the healthcare provider and prevent criticism. Recall periods are inconsistent between stud ies (e.g. number of doses missed over the last 3 days vs. the number of doses missed over the last 30 days). Consequently, when selfreporting methods are used to assess adherenc e, levels are frequently over-estimated. Although patients who admitted they have le ss than optimal adherence are almost always truthful, the reverse is not always true (Miller & Hays 2000b). Self-reported adherence was over-reported when compared to adherence measured by EDM in a study of 44 patients conducted by Melbourne and coll eagues (1999). Using an investigatordesigned questionnaire, patie nts self-reported an extrao rdinary amount of perfect adherence. Using EDM data, patients under-repo rted their degree of deviation from their stated dosing times. A benefit of self-reported adherence measurement is th e potential to reveal the reasons for missed or mistimed doses. C onsidering that clinical, behavioral and psychosocial factors are among the most impor tant factors that influence adherence (Chesney, 2003), self-report provides an opportunity to identify factors that might negatively affect adherence (Pow ell-Cope, Toney, & Montano, 2001). Researchers conducted a meta-analysis a nd found that self-reported measures reliably predicted clinical outcomes associ ated with adherence (Nieuwkerk & Oort,
24 2005). This information is consistent with earlier studies that examined the relationship between self-reported adheren ce and virologic outcomes, pr otease-inhibitor drug levels and other clinical outcomes (Kerr, Wals h, Lloyd-Smith, & Wood, 2005). While patientreported adherence has been consistently a ssociated with viral suppression, as has adherence measured by EDM and unannounced pill count, self-reported adherence questionnaires or interviews used in rese arch may fail to iden tify 20% to 28% of nonadherent patients (Bangsberg, Bronstone, & Hofmann, 2002). A number of studies have demonstrated a positive association between selfreported adherence and HIV RNA suggesting that self-reports ma y be a valid indicator of adherence (Haubrich et al., 1999; Montaner et al., 1998). Mannheimer and colleagues (2002) prospectively studied the correlat ion between self-reported adherence and successful response to HAART and found that a higher level of self-reported adherence over time was associated with better immunologic and virologic outcomes. While researchers concluded that self-reported medication adherence was a strong and independent predictor of virologic outcome, they also indicated that other methods of measuring adherence, such as the use of EDM, may allow for greater precision in measurement. A recent meta-analysis confirmed that despite significant study heterogeneity, the pooled association between self-reported HAART adherence and HIV RNA was statistically signifi cant (Nieuwkerk & Oort, 2005). The manner in which clinicians and re searchers communicate with patients regarding adherence may impact the patients response. Patients may provide more truthful information if the pers on collecting the data is not a direct member of the health care team or if the patient believes the data will not be reported to clinicians (Orrell,
25 2005). Questioning, whether verb al or in writing, that is carefully structured, nonjudgmental, culturally appropriate, and pos ed in a nonjudgmental manner and with the use of permissive language may elicit th e most accurate and truthful self-report data (Mannheimer, Friedland, Matts, Child, & Chesney, 2002). An example might include: Patients with complex medication schedul es like yours often mi ss doses of their medications from time to time. Can you tell me how many doses you missed in the last week? An alternative might include: Of the seven doses of medication you were prescribed to take last week, how many did you actually take? (Melbou rne et al., 1999). Patient self-report measures in the form of personal interviews or written questionnaires have many advantages includ ing low cost, minimal participant burden, easy speed of administration, flexibility in te rms of mode of administration and timing of assessment, and the potential to yield specifi c information about the timing of doses and adherence to food requirements. The specifi city of self-report measures is high, i.e., patients acknowledgment of nonadhe rence is generally credible (Bangsberg, Hecht et al., 2001). Self-report may not be feasible with some individuals such as the cognitively impaired (Simoni et al., 2006). Written medication diaries may increase the accuracy of self-report in patients who have difficulty remembering their pill-tak ing history. A benef it is the relative low cost associated with this method. However, one study suggested that patients did not consistently complete diaries and when they did, they tended to fill in the information immediately before a clinic visit. In one study utilizing diaries, only 25% of patients returned their diaries as in structed (Miller & Hays, 2000b). Several other methods have been used to obtain self-report adheren ce levels including computer-assisted self-
26 interviews, visual analog scales, self-report instruments and questionn aires, and clinical assessment. Each will be briefly discussed. Computer-Assisted Self-Interview Computer-assisted self-interview (CASI) technology may be an efficient way of obtaining self-reported data to identify HAART regimen errors and to monitor adherence. CASI involves a computerized interactive stru ctured interview that assesses patients' understanding of HIV medication regimens a nd adherence levels. To minimize literacy requirements, patients can listen to an audi o track which reads the interview text that normally would be presented visually on the computer screen. Photographs or graphics, rather than antiretroviral names alone, can be used to facilitate visual recognition of a patient's HAART regimen. If performed in conjunction with a clinician appointment, the CASI data can be used to provide valuable teaching information as well as an assessment of adherence (Bangsberg, Bronstone Chesney, & Hecht, 2002). CASI is time-efficient and may help dete ct nonadherence due to regimen errors or missed doses. In one study, CASI adheren ce assessment identified serious HAART regimen errors in up to 54% of patients. CAS I-based adherence assessment can facilitate intervention by alerting clinicians to potenti al adherence problems, prompting a more detailed discussion of adherence during a c linical visit (Bangsbe rg, Bronstone, Chesney, & Hecht, 2002). An adherence CASI has seve ral additional advantages over traditional self-report methods. Patients can be routinel y and periodically as sessed with a visual query of their understanding of, and adhere nce to, their HAART regimen with minimal use of clinician time. This may help to identif y patients with regimen errors or in need of focused or intense adherence intervention. A lthough there are expens es associated with
27 initial start-up of this method, ongoing expenses are typically minima l. Web-based CASI adherence assessment can be performed to identify difficulties with adherence more rapidly than otherwise possible with assessm ents that are only performed during actual clinic encounters. Visual Analog Scale Kalichman and his team (2005) used a pi ctographic visual analogue scale (VAS) to assess medication self-efficacy in a lowliteracy population and found that the scores were associated with behavioral measur es of medication adherence and HIV RNA response. Visual analogue scales may be appropriate for patients with language challenges as well as those with reading limitations. Giordano and his team (2004) compared an investigator-administered VAS in conjunction with a more complicated 3day medication recall instrument and unannounced pill counts in a group of marginally housed indigent patients who were on stab le HAART regimens. The VAS demonstrated good validity compared to unannounced pill count and HIV RNA, performed as well as the 3-day recall instrument a nd was easier to administer an d answer than other recall instruments. The VAS method offers several advantag es over the tradit ional recall method including decreased time requireme nt, ability to obtain data over a longer time frame and a lower response burden on the patient (G iordano, Guzman, Clark, Charlebois, & Bangsberg, 2004). Researchers compared the ac curacy of patient recall of adherence over 1, 3, 7, and 30-day intervals. Although it wa s expected that shorter periods of time would result in the most accurate recall, researchers found that the 30-day VAS
28 performed slightly better than other measur es of self-reported a dherence over shorter periods of time (Bangsberg, Bronstone, & Hofmann, 2002). Self-Report Instruments and Questionnaires Several adherence measurement instruments have been reported in the literature primarily in the context of c linical research trials. Mo st adherence questionnaires ask patients to recall the specif ic number of missed medication doses over a certain time period such as the last 2-7 days. Patients are typically asked to recall day-by-day and medication-by-medication doses or missed doses Table 2 summaries adherence selfreport instruments.
29 Table 2 Adherence Measurement Instruments Instrument Description Popula tion Studied Reliability/Validity Adult AIDS Clinical Trial Group (AACTG) Adherence Baseline Questionnaire (Chesney et al., 2000) 9-page self-report of beliefs about medications, social support, missed or late doses, selfefficacy, psychological distress, health habits, alcohol and drug use, sociodemographic characteristics and a 20-item symptom index. Takes approximately 10 minutes to complete. Approximately 62 items. HIV infected patients, 20% female, 30 nonwhite No detailed information available. Authors believe the instruments appear to be practical, acceptable to patients and investigators and should prove useful for efficient collection of data describing adherence to medications within clinical trials populations. Highly significant association was seen between self-report of missed doses and detectable viremia. Several correlates of non-adherence were identified. Adult AIDS Clinical Trial Group (AACTG) Adherence Follow Up Questionnaire (Chesney et al., 2000) 6-page self-report of missed or late doses, medication doses, food requirements or special instructions, reasons why doses were missed and a 20-item symptom index. Approximately 47 items. HIV infected patients, 20% female, 30 nonwhite Community Programs for Clinical Research on AIDS (CPCRA) Antiretroviral Medication Self-Report (Form 646) (Mannheimer, Friedland, Matts, Child, & Chesney, 2002) 7 day global recall of amount of each medication taken (all, most, about half, very few, none). Number of items varies based on number of ARV agents. Includs checklist of 10 possible reasons why ARV doses were missed. HIV infected patients, 20% female, 72% nonwhite. Not reported. Godins Self-Reported Questionnaire Assessing Adherence to Antiretroviral Mediation (Godin, Gagne, & Naccache, 2003) 9 main questions of which 3 address nonadherence. Developed in French and English. Predominantly HIV infected men who have sex with men Not reported, although authors stated, adequate psychometric properties (Godin et al, 2003, page 329) Morisky Medication Adherence Scale (MMAS) (Corless et al., 2005; Morisky, Green, & Levine, 1986) 4 brief yes or no questions that address barriers to medicationtaking and permit the clinician to reinforce positive adherence behaviors Limited use in HIV disease; primarily studied in patients with hypertension, asthma and hyperlipidemia Internal consistency, = 0.61 0.65 Patient Medication Adherence Questionnaire Version 1.0 (PMAQ-V1.0) (DeMasi et al., 2001) 6 items assess medication-taking behaviors; 25 items assess barriers and motivators to taking medication HIV infected patients, 85% male, 32% nonwhite. Internal consistency, = 0.79 Pictographic Medication SelfEfficacy Scale (Kalichman et al., 2005) Pictographic and color visual analogue scale for assessing selfefficacy for medication adherence. Uses 6 scenarios. HIV infected patients, 36% women, 99% African-American Internally consistent ( = 0.68); stability (2-week test/retest r = 0.63); evidence for convergent and divergent construct validity. Simplified Medication Adherence Questionnaire (SMAQ) (Knobel et al., 2002) 6 items based on Morisky scal e HIV infected patients, 72% male, 65% IDU 72% sensitivity; 91% specificity; likelihood ratio of 7.94 to identify nonadherent patients, compared with MEMS; Internal consistency, = 0.75; Interobserver agreement 88.2%, kappa 0.74.
30 The Adult AIDS Clinical Trial Group ( AACTG) developed both a baseline and a follow-up self-report adherence assessment instru ment for use in clinical trials. These instruments have been included in a number of AACTG clinical trials to date and have been widely disseminated to i nvestigators both in the United States and abroad (Chesney et al., 2000). The Community Programs for Clinical Research on AIDS (CPCRA) 7-day global recall adherence questi onnaire produced adherence da ta that predicted biologic outcomes including HIV RNA and CD4 lymphoc yte count. Adherence was associated with non-detectable HIV RNA levels, a change in HIV RNA levels and a change in CD4 lymphocyte counts over a 12 month period (M annheimer, Friedland, Matts, Child, & Chesney, 2002). Morisky, Green and Levine (1986) develope d an instrument to assess adherence to hypertension therapy which also addresses barriers to medica tion-taking. This tool has been incorporated into severa l studies involving HIV-infect ed patients and demonstrated success within this populati on (Corless et al., 2005; Gao & Nau, 2000). Knobel et al. (2002) developed the Simplified Medicati on Adherence Questionnaire (SMAQ) to identify non-adherent patients and determined the instrument to be adequate in most clinical settings. The Patient Medicati on Adherence Questionna ire (PMAQ) assesses medication-taking behaviors and barriers to adherence with HAART. Self-reported adherence derived from this instrument pr edicted virologic outcomes but the authors suggested additional refinement of the dimensi ons is needed (Boyle, 2003; DeMasi et al., 2001). Some researchers have either develope d their own assessment tools or have modified versions of other instruments, t hus complicating the ability to compare data
31 from different studies. Gau and Nau (2000) measured adherence using a Morisky-type scale and two other measures of self-repor ted adherence to evaluate accordance yet found discordant results. They recommend cauti on when comparing adherence rates between studies that use different met hods for assessing adherence. Using self-report, Hill et al. (2003) studi ed patients definitions of adherence, beliefs about consequences of nonadherence a nd reasons for current and past adherence behavior. They identified three categories of adherence: 1) consistent adherers; 2) currently adhering but with pr ior nonadherence; and 3) curr ently not adhering. They also identified nine patterns of adherence: 1) takes medication very rarely; 2) alternates between long period of taking and not taking me dication; 3) skips en tire days; 4) skips doses; 5) skips one type of me dication; 6) takes medication la te; 7) does not stick to food requirements or restrictions; 8) adheres to a purposely modified regimen and 9) adheres to an unknowingly incorrect regimen. Noting that patients have definitions of adherence that may be quite different from the definitions used by clinicians, they suggested that adherence questionnaires and assessment tools need to reflect the diversity of patient beliefs and patterns of medication-taking to more accurately measure adherence or less than optimal adherence In a large meta-analysis Simoni and co lleagues (2006) observed a robust pattern of association between self-re ported adherence and HIV RNA. In 84% of recall periods, self-reported adherenc e was associated with HIV RNA based on odds ratios or simple measures of correlation. The association was statistically significant across a variety of self-reported measures, administration modalities, and recall periods. These findings are consistent with the conclusions of a meta -analysis of adheren ce studies performed by
32 Nieukerk and Oort (2005). The associati on between self-report and CD4 cell count was less consistent, a finding that was not entirely unexpect ed since HIV RNA and CD4 count generally correlate, but discordant resu lts are common. They concluded that even brief self-report measures of HAA RT adherence can be robust. Clinician Assessment Studies examining healthcare provider abili ties to predict their patients adherence have been inaccurate and overly optimistic leading to the misidentification of nonadherent patients (Bangsberg, Hecht et al ., 2001; Haubrich et al., 1999). Paterson (2000) found that physicians predicted adherenc e incorrectly for 41% of patients compared with nurses who predicted it incorrectly for 30% of patients. Miller and colleagues (2002) found that clinicians overestimated medication adherence by almost 9% and inadequately detected poor adherence. Consequently, clinicians missed opportunities to intervene with appropriate adhe rence interventions. Miller and Hays (2000b) suggest ed that clinicians subjecti ve assessment of adherence may be as problematic as a patients self-re ported adherence. HIV care providers in routine clinical practi ce rarely predicted pati ent adherence. This often means that when health care providers do not use patient reports of adherence, they are leaving the most critical determinant of HIV treatmen t outcome to chance (Bangsberg, 2006). Objective Measures of Adherence In contrast to subjective measures, obj ective measures rely on data recorded independently of the patient (Orrell, 2005). The following objective measurement methods will each be reviewed: directly observed therapy, therapeutic drug monitoring,
33 biomedical examination, pill counts, electr onic monitoring devices, and pharmacy refill data. Directly Observed Therapy One way of assessing adhere nce is participation in di rectly observed therapy (DOT) in which administration of each dose is directly monitored. DOT has been used in the treatment of tuberculosis for decades and was later applied to HIV treatment (Mitty, Stone, Sands, Macalino, & Flanigan, 2002). The first randomized controlled trial of community-based DOT in HIV care revealed sign ificantly higher levels of self-reported adherence, higher CD4 lymphocyte response, and greater HIV RNA reduction than those not participating in DOT (Altice, Mezger, & Bruce, 2003). DOT programs, often modeled after those used in tuberculosis trea tment programs, may not be practical in HIV care due to the large and growing numbers of HIV-infected patients since these programs are labor intensive, expensive and can be pe rceived as intrusive (Liechty & Bangsberg, 2003). While DOT may be more feasible with the increasing nu mber of once a day HAART regimens, many of th ese daily regimens are administered at bedtime which increases the impracticality of DOT for this group of patients. Therapeutic Drug Monitoring (TDM) Monitoring of plasma and urinary drug leve ls has been proposed in clinical and research settings. Serum levels of some drug metabolites provide evidence that individuals are taking medication but they do not provide specific information about the number of doses missed or taken, individual pa tterns of missed doses, or adherence to a medication based on a time schedule This method is prone to wide individual variation in drug pharmacokinetics related to the prope rties of drugs, drug-dr ug interactions and
34 variations in drug absorption. Moreover, drug levels may onl y reflect recently taken medication doses rather than long-term patte rns of drug levels (Miller & Hays, 2000a). Low drug levels have been associated with self-reported nonadherence and virologic failure (Murri et al., 2000; Nieuwkerk et al., 2001). TDM also is limited by a lack of technologic standardization of assays as well as limited general availability of the laboratory assays (Acosta & Gerber, 2002). Some researchers have attempted to exploit the biologic changes induced by antiretroviral agents to indirectly measure ad herence. Stavudine and zidovudine can raise the mean corpuscular volume, didanosine can alte r uric acid levels and both indinavir and atazanavir can increase bilirubin levels. While these data provide some degree of objective measurement, they are only marginally sensitive and specific markers of medication adherence and provide little inform ation about individual patterns of missed doses (Cinti, 2000; Miller & Hays, 2000a). Biomedical Examination Laboratory measurement of CD4 lymphoc ytes and HIV RNA levels has been used as indirect measures of adherence. Wood and his colleagues (2004) determined that adherence was the strongest independent pred ictor of an increase in CD4 lymphocyte count after beginning HAART therapy. Unfo rtunately, biomedical markers including CD4 lymphocyte cell counts and HIV RNA levels do not always correlate with adherence levels. Patients can have a drug-sensitive vi rus and be adherent to their HAART regimen yet still experience HAART failure due to th e development of drug-resistant HIV strains, drug interactions and unfavorable pharmac okinetic properties (Miller & Chang, 2002). Additionally, laboratory testing can be e xpensive. Laborator y measures may be
35 considered more useful when used in combin ation with other adherence measures such as pill counts, self-report assessm ents and EDM (Cinti, 2000). Pill Counts Pill counts frequently are used in conjunction with clinical drug trials and provide an objective means of assessing the number of pills removed from the bottles. Pill counts are easy and inexpensive to perfor m (Miller & Hays, 2000b). Disadvantages of pill counts are that they can be time consuming for clinical and research staff; they do not guarantee that the pills were taken as pr escribed; patients may knowingly empty the bottle prior to the visit in anticipation of a p ill count; they may forget to bring bottles to the clinical site; and some may perceive pill counts as intrusive (Berg & Arnsten, 2006). Unannounced pill counts may provide a more accurate assessment of adherence rates than self-report. In one study this method was more predictive of HIV RNA than self-reported adherenc e measures and performed well compared to electronic data monitoring using computerized medication cap s (Bangsberg et al., 2000). Unannounced pill counts may not be practical in many settings since home visits are usually required. Electronic Monitoring Devices Computer-assisted electroni c drug monitoring devices, al so commonly referred to as electronic data (and sometimes drug) m onitoring (EDM) devices, are frequently used in research settings and to a lesser extent clinical settings. Small electronic chips embedded in the caps of pill bottles record each time a bottle is opened or closed and the length of time the bottle is open. Data is dow nloaded to a personal co mputer periodically for analysis. One of the more common EDM products is the Medication Event Monitoring System (MEMS) (New York State Department of Health, 2001).
36 Data collected from EDM equipment has been found to correlate highly with concurrent HIV RNA (Samet Sullivan, Traphagen, & Ickovics, 2001). Limitations related to EDM include expense and the possibility of under-reporting adherence in patients who elect to remove more than one dose at a time. Under-reporting may occur when patients remove medication to fill a pill bo x or remove extra doses in planning to be away from home for an extended period of tim e. EDM also assumes that the patient actually takes each removed pill. Over-reporti ng can also occur as pills may be removed but not swallowed and bottles may be opened without removing pills. EDM methods have additional drawbacks including in convenience, patient dissatisfaction and confidentiality concerns (Mannheimer, Frie dland, Matts, Child, & Chesney, 2002). EDM is rarely used in clinical practice due to the expense of the equipment. Pharmacy Refill Monitoring Pharmacy refill data can serve as an adherence measure by providing the dates on which antiretroviral medicati ons were dispensed. This measure is based on a straightforward premise that when a patient does not receive timely refills of a drug from the pharmacy, he or she is either not taking medication between refills or is missing doses such that a given prescription lasts longer than it should (Turner, 2002). Researchers have studied the number of prescr iptions picked up, timeliness of medication pickup and gaps in medication based on refill data. These measures are usually calculated based on the number of days supply obtaine d divided by the total number of days in the period or the number of refills obtained divided by the expected number of refills over a given time period (Steiner & Prochazka, 1997). Low-Beer, Yip, OShaughnessy, Hogg and Montaner (2000) examined pharmacy dispensing data and found a significant linear trend
37 in viral suppression across ordered categories of adherence. Pharmacy refill adherence rates of 95% or greater were associated with high virologic success; success rates decreased sharply with d ecreasing levels of adhe rence to refills. Several studies have used pharmacy data to assess adherence among patients with HIV disease. One study found that self-repo rted HAART adherence correlated with pharmacy dispensing records and predic ted viral suppression at levels > 97% (Fairley, Permana, & Read, 2005). Grossberg and colleag ues (2004) demonstrated that adherence, as measured by time-to-pharmacy refill, was able to distinguish an HIV RNA impact among individuals self-reporting perfect adhere nce. Patients who were adherent to HAART as measured by consistent pharmacy refills for greater than 4 months were significantly more likely to ach ieve virologic control and im munologic benefit than were less-adherent patients (Maher et al., 1999). Using pharmacy-based adherence measures, Kitahata et al. (2004) determined that higher levels of adherence to HAART were significantly associated with longer time to virologic failu re, greater increase in CD4 lymphotcyte count, and lower risk of progres sion to clinical AIDS or death. After controlling for other factors, patients with lo w adherence had over five times the risk of disease progression in patients w ith moderate adherence, or patients with high adherence. Assessing refill records is non-intrusive and reduces the possibility of bias in the research process as subjects are usually not aware that their behavi or is being monitored. However, like other measures of adherence, pharmacy refill pickup does not assure that the patient actually took the medication as prescribed (Miller & Hays, 2000b). Combined Methods of Adherence Measurement
38 To address the limitations of any one m easurement approach, some researchers have suggested that adherence measuremen t methods be combined (Kerr, Walsh, LloydSmith, & Wood, 2005). The use of medication diaries with computer-assisted selfinterviewing gave insight into patients adherence patterns (Hugen et al., 2002). Selfreported adherence data has also been shown to enhance data obtai ned from electronic monitoring methods (Bangsberg et al., 2000). Liu and colleagues (2001) examined differe nt adherence measures applied to the same patient and found that different methods of measurement suggested different levels of adherence. Adherence was underestimated by EDM and overestimated by pill count and interview. Data obtained from EDM, pill counts, and interviews were subsequently merged into a composite adherence score (CAS ). While adherence as measured by CAS, EDM, pill count, and interview were associ ated with achievement of undetectable viremia within six months of initiating HAART therapy, the CAS demonstrated the strongest predictive relationship. Alt hough the summary measure combining several measures was more strongly related to a cl inical response, they suggested a more practical measurement method is needed for clinical use. Berg and Arnsten (2006) suggested that adherence is especially difficult to measure because it is composed of several distinct behaviors. Component adherence behaviors include obtaining refills, ingesti ng the right number of pills, ingesting pills within an effective dosing interval, and ingest ing pills in accordance with any appropriate dietary requirements. Individual measures of adherence frequently measure just one single aspect of adheren ce behavior. This phenomenon of construct underrepresentation occurs when a measure fails to assess important dimensions of the
39 construct in question (Hubl ey & Zumbo, 1996). Although some adherence measures such as EDM provide the ability to measure se veral aspects of adhere nce, the data are not generally analyzed in this manner. EDM a nd other measures are vulnerable to another validity threat caused by measuring unrelated constructs. The term construct irrelevant variance is used when a measure contains excess variance attributable to unrelated constructs (Berg & Arnsten, 2006). No single method has been established as the gold standard for measuring adherence. Each method has advantages as well as disadvantages. The HIV treatment guidelines published by the HIV Medicine Associati on of the Infectious Diseases Society of America suggested that once an adherence assessment method has been selected, it should be used consistently to monitor each pa tients adherence at e ach visit (Aberg et al., 2004). Chesney (2006) indi cated that it is unlikely that a single optimal measurement of adherence can be found as the reasons fo r measuring adherence vary based on whether the assessment is for research or clinical pu rposes and require further refinement based on the research questions bei ng investigated or the clini cal needs being addressed. Consequently, it is unlikely that a single opt imal intervention can be developed because the reasons for nonadherence are as diverse as the populations affected by HIV disease. In summary, there is no clear and uni versal method to rigorously measure individual patients adhe rence. Rigorous adherence measurement requires interdisciplinary collaboration between social scientists and HIV researchers. Improving the measurement of HAART adherence would facilitate the development and evaluation of adherence-improving interven tions with standardized and empirically tested adherence measures (Berg & Arnsten, 2006).
40 Measurement of Treatment Outcomes The success as well as failure of HIV trea tment can be evaluated using virologic, immunologic, and clinical criteri a. Virologic indicators app ear earliest af ter initiating HAART and are represented by a decrease (in th e case of success) or increase (in the case of failure) in HIV RNA. Immunologic treatme nt success or failure usually occurs next and is measured by an increase (success) or decrease (failure) in the CD4 lymphocyte count. Although clinical treatme nt failure, if it occurs, usua lly becomes apparent much later, clinical success can often be assessed early after the initiati on of HAART as many patients experience an improvement in HIV-related constitutional symptoms, such as weight loss, generalized lym phadenopathy, fever, and night sweats (Hoffman, Rockstroh, & Kamps, 2006). Measurement of Virologic Outcomes Virologic success is defined as a reduc tion of HIV RNA to below the level of detection. This is based on an understanding that the more ra pid and greater the decrease in HIV RNA, the longer the therapeutic eff ect (Kempf et al., 1998; Powderly et al., 1999). Commercially available assays whic h measure HIV RNA vary based on the lower level of detection and dynamic ranges. The most common lower-level thresholds report HIV RNA levels as less than (<) 50 copies, < 75 copies and < 80 copies based on the testing methodology and equipment being used. While a lower level threshold of <50 HIV RNA copies is most common, there ar e no data suggesting less virologic success when HIV RNA is measured with alternativ e thresholds (Hoffman, Rockstroh, & Kamps, 2006). Table 3 describes the common HIV RNA testing methodologies.
41 Table 3 Quantitative Plasma HIV RNA Techniques Technique Test Name Manufacturer Dynamic Range HIV RNA PCR (RT-PCR) Amplicor HIV-1 Monitor Test version 1.5 Roche < 50 750,000 copies/ml Branched chain DNA (bDNA) Versant HIV-1 RNA 3.0 Bayer < 75 500,000 copies/ml Nucleic acid sequence-based amplification (NASBA) NASBA or NucliSens HIV-1 QT bioMerieux <80 3,500,000 copies/ml Adapted from Bartlett & Gallant (2005) HIV RNA is the most commonly used variable used by clinicians to assess patient measures of adherence but it is also affected by antiretro viral drug resistance and drug bioavailability (Wagner et al., 2001). Seri al measurements of HIV RNA are routinely used to monitor the effectiveness or failure of therapy and help to determine if the beneficial effect of treatment is being maintained or lost. A change of > 3-fold or > 0.5 log10 copies/ml is considered significant (Bar tlett & Gallant, 2005; St einhart, Orrick, & Simpson, 2002). Measurement of Immunologic Outcomes Immunologic treatment success is broadly defined as an increase in the CD4 lymphocyte count. It is difficult to indi vidually predict the imm unological success of therapy for patients on HAART as it varies significantly from one person to another. Although individual research studies may ha ve precise operational definitions of immunologic success, no standard definition exists (Hoffman, Rockstroh, & Kamps, 2006). Immunological treatment success is not always associated with maximal viral
42 suppression as even partial suppression can result in a signif icant CD4 lymphocyte response (Ledergerber et al., 2004). Serial measurements of CD4 lymphocyte s are routinely used to monitor the immunologic response to therapy. In the ab sence of HAART, the average rate of CD4 lymphocyte decline is 4% per year for each log10 HIV RNA copies/ml. There is a great variability in CD4 lymphocyte test results. For example, the 95% confidence range for a true count of 200 CD4 lym phocyte cells per millimeter3 is 118-337 cells per mm3 (Bartlett & Gallant, 2005). Several factors can influen ce the variability of CD4 lymphocyte counts including laboratory anal ytical variation and seasonal and diurnal fluctuations. CD4 lymphocyte counts are also used to stage HIV disease and guide prophylactic treatment. A CD4 lymphocyte count <200 copies/mm3 indicates severe immunodepression and is a di agnostic marker of AIDS (Bartlett & Gallant, 2005; Steinhart, Orrick, & Simpson, 2002). Measurement of Clinical Treatment Outcomes Clinical treatment success is dependen t on virologic and immunologic success and has been reported in numerous studies (L edergerber et al., 2004; Salzberger et al., 1999). Clinical response is not always easy to assess as there is no way to show what might have occurred if treatment had not b een initiated. Clinical success is usually evaluated based on either the absence of clinical endpoints such as AIDS-defining illnesses or death or an improvement in, or resolution of, HIV-related constitutional symptoms such as weight loss, generalized lymphadenopathy, fever, and night sweats (Bartlett & Gallant, 2005; Hoffman, Rockstroh, & Kamps, 2006).
43 Impact of Medication Adherence on Immunologic and Virologic Outcomes Adherence to HAART has been shown to be an important predictor of virologic suppression and of clinical outcomes (Gross, Bilker, Friedman, & Strom, 2001; McNabb et al., 2001; Paterson et al., 2000). HAART adhe rence is the second strongest predictor of progression to AIDS and death, after CD4 lymphocyte count (Bangsberg, Perry et al., 2001; Garcia de Olalla et al., 2002; Hogg et al., 2002). In a meta-analysis of randomized controlled trials (RCTs ) of interventions for adherence to HAART, Simoni and colleagues (2006) found that across 19 RCTs w ith more than 1800 participants, those who received an adherence intervention were 1.5 times as likely to report 95% adherence and 1.25 times as likely to achieve undetect able HIV RNA levels as participants in comparison conditions. CD4 lymphocyte response can be some what delayed following initial HAART initiation. For this reason, many experts beli eve that HIV RNA is the best measure of therapeutic response to HAART (Nieuwkerk & Oort, 2005). Bartle tt and Gallant (2005) believe that the CD4 lymphocyte response is the best clinical prognostic indicator. Adherence Interventions : Review of Studies Introduction Increasing recognition that medication adhe rence is a determinant of treatment outcomes has generated a number of studies investigating methods to support and improve adherence. While early research stud ies on this topic were primarily based on small pilot and feasibility st udies and had minimal empiric validity, there has been an increase in the number of RCTs with adequa te sample sizes emer ging over the last few years (Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). In general, patients who
44 receive interventions for adherence are more likely to achieve higher levels of adherence and are more often able to achieve undetect able HIV RNA levels than participants in various controlled conditions (Chesney, 2006). Adherence interventions are costeffective, and are likely to pr ovide long-term survival benefit to patients (Freedberg et al., 2006). This section will review and summari ze studies related to HAART adherence interventions. Interventions will be categor ized as 1) patient education and counseling strategies; 2) directly obse rved therapy; and 3) adhere nce devices and reminders. Studies are often characterized as : 1) cognitive (designed to teac h, clarify, or instruct); 2) behavioral, such as those desi gned to shape, reinforce, or influence behavior; or 3) affective, such as those designed to optimize social and emotional support. Patient Education and Counseling Interventions The majority of adherence interventions reported in the literature involve dedicated time with patients to plan fo r and support medication adherence. The frequency and nature of these interventions va ried, but those that appeared effective were characterized by an initial education se ssion followed by ongoing sessions maintained regularly over the course of treatment (Machtinger & Bangsberg, 2006). These studies typically involved cognitive interventions or educational interventi ons targeting patient knowledge of drug therapy and employed methods such as counseling by a nurse educator, clinical pharmacist, or physicia n. Cognitive interventions typically provided general information such as dosing instruc tions, medication descri ption, drug interaction information or general information about HAART options.
45 A wide range of behavioral strategies ha ve been implemented including the use of pillboxes, using a register to record admini stered medications, role-playing medication schedules and schedule adjustments, using behavioral problem-solving groups, teaching self-monitoring skills, and identifying risk f actors for nonadherence. Some studies used a combination of interventions including cogni tive and behavioral methods such as describing dosing instructions and graphs of HIV RNA levels or educational sessions with an adherence counselor a nd a weekly pill container. Some studies contained three interventions including cognitive, behavioral, and affective techniques. Several published studies have tested educational interventions involving healthcare professionals teaching patients about their medications, the importance of adherence, and methods to strengthen adhere nce. While several researchers found that educational interventions had a sustained impact on adherence (Goujard et al., 2003), others found minimal or no effect (Rawlings et al., 2003; Remien et al., 2005). Virologic and immunologic impact was inconsistently observed. Adherence measurement methods also varied among thes e studies with EDM and self-report being most common. One researcher utilized cuedosing (timing doses around meal times or regular daily activities) and monetary reinforcement to remind patients to take their medications and observed an improvement in adherence that was not sustained and returned to baseline with discontinuan ce of the intervention (Rigsby et al., 2000). Two studies examined the effect of pharm acist-led adherence sessions (Haddad et al., 2000). Although patients who received the intervention self -reported higher levels of adherence, virologic improvements were only seen in one study (Rathbun, Farmer, Stephens, & Lockhart, 2005). Knobel (1999) administered individua l advice regarding
46 adherence and the effect was m easured with structured interv iew and pill counts. Those who received individual counseling had signi ficantly better adherenc e rates than those who did not, but there was no significant difference in virologic response. Several studies have tested motivational interviewing and cognitive-behavioral problem-solving approaches to improve adhere nce. Adherence was consistently higher in patients who received these interventi ons. While Safren et al. (2001) found little difference between patients that received a single intervention session compared with patients who simply maintained a pill diar y and completed an adherence questionnaire, patients who received motivational intervie wing led by nurses reported higher medication adherence than those receiving usual care a nd were more likely to follow the medication regimen as prescribed by their health care provider (DiIor io et al., 2003). In another study, patients received 10 sessions of cognitive-behavioral stress management and expressive supportive thera py (Jones et al., 2003). Participants were assessed on self-reported medication adhere nce over seven days along with coping strategies and beliefs related to HAART. Patients with low baseline adherence that received the intervention significantly incr eased their mean self-reported adherence by approximately 30%. Those in the usual care group showed a non-signi ficant increase in adherence. After receiving monthly cognitive behavior therapy se ssions over a one year period, Weber (2004) found that patients m ean adherence as measured by EDM was similar between the intervention and sta ndard care group. While the proportion of patients with adherence levels > 95% was significantly higher in the intervention group, virologic outcomes in both groups were similar.
47 Smith, Rublein, Marcu, Brock and Chesney ( 2003) examined the effect of a selfmanagement intervention based on feedback of adherence performance and principles of social cognitive theory on adherence. I ndividuals in the self-management group were significantly more likely to take 80% or more of their doses each week than individuals in the control group as measured by EDM. In summary, some improvement in adherence has been seen with education and counseling-based interventions but the results were inconsistent and frequently diminished when the intervention was terminated. Significant immunologic and virological improvements were inconsistently observed. Directly Observed Therapy Directly observed therapy (DOT) has been studied as an a dherence intervention based on its successful use in treating nonadherent tuberculosis patients. Fischl (2001) and her team compared patients receiving DOT in a correctional facility to those receiving standard outpatient clinic services and found th at patients who received DOT had a significantly higher chance of achievi ng undetectable HIV RN A than those that received standard clinic care. Altice, Mezger and Bruce ( 2003) compared DOT for oncedaily dosing, modified DOT (twice-daily dosing in which one dose wa s give via DOT) and standard care. The patients receiving DOT had significant im provements in three-day self-reported adherence, six-month medi an CD4 lymphocyte response, and six months median reduction of HIV RNA. In another study, pre gnant women who were identified as being at very high risk for HAART nonadherence and consequent mother-to-child transmission
48 were given DOT during the third trimester of pregnancy. Clinical outcomes of no perinatal transmission of HIV, suppression of HIV RNA, and receipt of appropriate antiretroviral agents during labor were simila r to those that received standard care (Bryant, Collingham, & Till, 2004). While studies of DOT have resulted in improvements in clinical outcomes associated with HAART, DOT programs may not be appropriate for most clinical setting as they are expensive, labor-intensive and fr equently perceived by pa tients as intrusive (Liechty & Bangsberg, 2003). Machtinger and Bangsberg (2007) believe that the best candidates for DOT are those with low motivat ional states who have experienced failure with less intensive adherence support a nd who have advanced HIV disease. Adherence Devices and Reminders A number of devices are av ailable to help patients adhere to their medication regimen including medication organizers such as pillboxes, reminder devices such as alarm watches and pagers, and visual medica tion schedules. Golin and her team (2002) found that patients who used more adhe rence aids were more adherent. The manufacturers of electronic drug monitoring de vices have even added clocks and alarms to their equipment to help remind patients to take their medication as prescribed (Miller & Hays, 2000a). Most devices are simple, in expensive, and easy to integrate into the routine care of patients. Pill boxes allow patients to organize their doses of medication in a convenient location. They eliminate the need to carry multiple medication bottles and provide a means to verify whether doses have been taken. Clinicians can monitor for nonadherence if patients take pillboxes to c linical appointments. Some pharmacies
49 provide medications prefilled into weekly or monthly organizers (Machtinger & Bangsberg, 2006; Machtinger & Bangsberg, 2007). While the success of these de vices has primarily been re ported based on clinical and field experience, several re search studies have been con ducted related to this area. McPherson-Baker et al. (2000) pr ospectively studied patients who participated in weekly sessions using pillboxes combin ed with monthly individual ized adherence counseling. After five months, those receiv ing the intervention had a sign ificant improvement in their adherence as measured by pharmacy refill data and fewer hospitalizations. Because many patients cite forgetting as a primary reason for missing doses of HAART, reminder devices such as alarms on watches, pagers and other electronic technology are recommended to provide mu ltiple daily reminders (Chesney, 2000; Chesney, Morin, & Sherr, 2000). Andrade et al. (2005) measured the effect of a memory-prompting device combined with mont hly adherence counseling on adherence to HAART in memory-intact and memory-impaire d subjects in a prospective RCT. Mean adherence scores as measured by EDM di d not differ between the intervention and control group. However, a subset of me mory-impaired patients who received the intervention had significantly higher levels of adherence. Safren and his team (2003) tested a customizable reminder system using webbased pager technology to increase and maintain adherence in patient s with pre-existing adherence problems. After a two-week monito ring period with EDM, participants with less than 90% adherence were ra ndomized to continue monito ring or to receive a pager. Compared to standard care, the group who received the pagers had greater improvements
50 in adherence through the first three months but adherence at the end of the study was still poor in both groups. Safren suggested that mo re intensive interventions are required for patients with pre-existing problems. Visual medication schedules contain pictorial displays of HAART agents superimposed on calendars as visual reminders of which pills to take and at what times. Although not tested in patients receiving HAAR T, Schillinger (2003) found that these visual schedules improved outcomes in patients receiving anticoagulation therapy (another chronic disease state involving daily medication that requires high levels of adherence). Qualitative Reviews and Meta-Ana lyses of HAART Interventions The literature related to HAART adhere nce interventions has been reviewed several times. Early qualitative reviews i ndicated that reports were based primarily on small pilot and feasibility studies and offere d few prescriptive guidelines with minimal empiric validity. While later reviews high lighted the improved rigor of the studies, considerable variation in sampling and asse ssment strategies, intervention components, and findings was noted (Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). Fogarty et al. (2002) published the first co mprehensive literature review all of published articles reporting inte rventions designed to increase adherence to HAART. Although 16 interventions were identified employing a wide range of behavioral, cognitive and affective strategies, only 11 in cluded data on intervention and efficacy and the effects of these interventi ons were generally weak.
51 Simoni, Pantalone, Frick and Turner ( 2005) performed a meta-analysis of 15 randomized controlled trials related to adherence interventi ons and found some significant differences in either adherence or clinical impact between the intervention and control arms in 10 of the studies. They noted several significant c oncerns: 1) the findings were difficult to interpret due to the hetero geneity in the studies; 2) the duration of treatment intervention varied from 1 to 10 sessions with ongoing follow-up ranging anywhere from 1 day to more than 1 year; 3) the methods used to assess adherence varied from different types of self -report to EDM; and 4) m easurement of immunologic and virologic response was uncommon. Improve ment in adherence was not commonly sustained. Unfortunately, findi ngs from similar interventions were inconsistent. For example, in two studies, cognitive-behavioral treatment was part of a successful strategy (Safren et al., 2001; Weber et al., 2004) but in two others it was not (Jones et al., 2003; Murphy, Lu, Martin, Hoffman, & Marelich, 2002). Simoni's (2005) review of the literature suggested a lack of empirical data necessary to make strong recommendations regarding the most efficacious way to improve adherence to HAART. Simoni and her team (2006) conducted another meta-analysis to determine whether behavioral interven tions addressing HAART adhere nce were successful in increasing the likelihood of a patients attaining 95% adhe rence or undetectable HIV RNA. Nineteen studies with a total of 1839 participants met their selection criteria of describing a randomized controlled trial am ong adults that evaluated a behavioral intervention with HAART adherence or HIV RNA as an outcome. Random-effects models indicated that across studies, those who received an adherence intervention were 1.5 times more likely to report 95% adherenc e and 1.25 times more likely to achieve an
52 undetectable HIV RNA compared to participants who did not receive an intervention. The intervention effect for 95% adherence was significantly stronger in studies that used recall periods of 2 weeks or 1 month as compared to 7 days or less. They concluded that more research is needed to identify the mo st efficacious intervention components and the best methods for using them in actual clin ical settings (Simoni, Pearson, Pantalone, Marks, & Crepaz, 2006). Amico, Harman and Johnson (2006) perf ormed a research synthesis of HAART intervention outcome studies published between 1996 and 2004. Effect sizes were calculated for each study outcome resulting in 25 immediate post-intervention outcomes and an additional 13 follow-up effect sizes. They found small effect size (d = 0.35, odds ratio [OR] = 1.88) that varied considerably across studies. In terventions that specifically enrolled participants with known or an ticipated problems with HAART adherence demonstrated medium effects on adherence (d = 0.62, OR = 3.07). Inte rventions that did not target their part icipants on similar criteria had small effects (d = 0.19, OR = 1.41). Adherence improvements showed no tendenc y to decay with time. The authors concluded that adherence intervention outco me studies must carefully delineate their target populations because defining indivi duals as "on HAART" does not provide the level of specificity needed to design and implement effective adherence interventions. Given the relatively small effects obser ved from studies of single adherence interventions and in an effort to expand th e breadth of adherence issues addressed by these interventions, combinations of adhe rence interventions are suggested by many adherence experts. Studies of patients with other chr onic diseases suggest that
53 approaches addressing only one factor relate d to adherence will not be as powerful as interventions addressing multiple factors (Miller & Hays, 2000a). Despite the need for programs and proce dures that support or enhance adherence to HAART, little evidence exists about the extent to which c linical practices have been able to incorporate adherence interventions into their routine care. Investigators conducted a survey of clinical care settings in New York and Connecticut and determined that the current standard of car e is to provide only minimal levels of adherence services. They also found that ad hoc adherence suppor t was frequently offered on an as-needed basis (Harman, Amico, & Johnson, 2005). Th ese findings support the need for the ongoing development of adherence interventions that are easily translatab le to real-life clinical practice. In some cases an intervention can become the standard of care despite the empiric data demonstrating its efficacy. In these cases it may be considered unethical to assign patients to the control arm of a trial. Fo r example, randomized controlled trials have provided evidence that behavioral interven tions improve adherence to HAART. Such interventions are increasingly considered the standard of care, making additional randomized trials less likely (Petersen, Wang, van der Laan, & Bangsberg, 2006). In the absence of conclusive empirical data clinicians have frequently turned to adherence strategies recommended by expert s which are based on limited data, research from adherence in other disciplines, clin ical practice experience and demonstrated correlates of adherence (Simoni, Pantalone, Frick, & Turner, 2005). For example, the Best Practices Guide, publishe d online by the American Public Health Association (Jani,
54 2002) proposes a practical four st ep approach in the management of adherence: 1) assess factors that may influence a dherence and function as potenti al barriers; 2) develop and maintain a therapeutic alliance with the patien t; 3) monitor the level of adherence using multiple measures; and 4) implement multiple targ eted interventions to resolve barriers to adherence. Chesney (2003), Turner (2002) and the American Psychological Association (1997) offered similar adherence manageme nt guidelines and recommendations which are summarized in Table 4.
55 Table 4 Adherence Management Guidelines and Recommendations Turner APA Chesney Simplify and explain the treatment regimen. Clarify the regimen. Deliver an introductory statement. Provide reminder devices. Tailor it to individual lifestyles. Confirm understanding of the regimen. Discuss potential side effects. Facilitate interaction with clinic staff. Assess adherence. Provide social support. Identify and remove personal barriers to adherence. Ask about reasons for missing doses. Treat concomitant psychological disorders and substance abuse problems. Refer patients with special needs such as substance abuse to appropriate treatment. Ask about medication side effects or other problems. Enhance self efficacy: offer positive feedback for new skills, demonstrated problem-solving and ways to integrate the regimen into their lives. Create a social environment conducive to adherence: enlist support from patient's social network and maintain support of the clinical team. Adapted from Turner (2002) Adapted from APA (1997) Adapted from Chesney (2002) Summary Improving adherence to HAART may re quire a combination of methods appropriate to the patient and clinical setting. Alterable factors known to impact adherence, such as depression, substance abus e, and the therapeutic relationship between patient and provider should be addressed in a proactive and ongoi ng manner. Adherence interventions should include dedi cated educational and collaborat ive time with patients to
56 plan for medication adheren ce and to maintain necessary support and collaboration throughout the course of treatment. In this way, problems such as side effects can be addressed, medications simplified or cha nged if necessary, a nd adherence devices supplied as deemed appropriate. Most of the adherence interv ention strategies studied to date have focused on factors di rectly related to patient beha viors. Other variables known to impact adherence have not been thoroughl y studied including factors related to the healthcare provider, the patient-provider relationship, factors related to the treatment regimen or illness, environment factors and contextual factors (Simoni, Pantalone, Frick, & Turner, 2005). As successfully tested interventions emerge in the literature, it is critical that the information be disseminated into clinical practice. The issue of efficacy versus effectiveness will need to be addressed becau se what works successfully in a researchbased trial may not work in clinics which face challenges such as limited staff and resources as well as diverse patient popul ations (Simoni, Pantalone, Frick, & Turner, 2005).
57 CHAPTER THREE: METHODS Introduction This chapter presents the research met hods and procedures for this study. It includes treatment conditions, background informa tion related to the structured adherence intervention, the study design, description of the study popula tion and setting, inclusion and exclusion criteria, data collection procedur es, data management, and the data analysis plan. Treatment Conditions Overview of the Study This retrospective compar ative study compared treatment response, pharmacy refill adherence, and self-repor ted medication adherence between two groups of patients: those participating in an AI DS Drug Assistance Program (A DAP) and those participating in a Medicaid-funded medication access progr am. The ADAP served as a structured adherence intervention (SAI) based on procedural and admi nistrative processes required by the state-managed program. Those patien ts receiving antiretroviral medications as part of the Medicaid-funded pr ogram were considered usual care as this program did not contain systematic procedur al and administrative condi tions which could impact adherence. A number of other variable s can impact medicatio n adherence including adherence interventions (a dherence counseling, educati on, and aids), ARV-related factors, sociodemographic factors, and HIV dis ease specific factors. Figure 1 depicts the conceptual model developed to structure this study based on existing research findings.
58 Figure 1. Conceptual Model for Evaluating the Effects of a Structured Adherence Intervention to HAART on Adherence and Treatment Response Outcomes.
59 Structured Adherence Intervention Introduction: National AIDS Drug Assistance Program The AIDS Drug Assistance Program (ADAP) is a federal program administered by each state to provide medications for the tr eatment of HIV disease. Eligibility to participate in the ADAP is based on the lack of adequate health in surance and financial resources necessary to cover th e cost of medications. While some clients are enrolled in ADAP on a long-term basis, others participat e temporarily while they await acceptance into other insurance programs. Each state AIDS Drug Assistance Program is unique in which medications are included in its form ulary and how those medications will be distributed (Department of Health and Human Services, 2007). Florida AIDS Drug Assistance Program The ADAP for the State of Florida is centr ally administered by the Bureau of HIV/AIDS in Tallahassee. The most populat ed counties within th e state have local ADAP offices based in the respective county health department to serve the nearby residents. Smaller counties with lower numbers of HIV-infected patients are served via a central pharmacy in Tallahassee. Program po licies and procedures are published in the ADAP Program Manual and serve as the operating standards for each ADAP office within the State (Florida De partment of Health, 2007). The goals of the Florida ADAP are to: 1) establish a program to provide therapeutics to treat HIV diseas e or prevent deterioration of health arising from HIV; 2) provide access to HIV treatments for low in come, indigent persons who have no other resource to attain needed medications; 3) facilitate access to the program; 4) provide outreach to individuals with HIV and thei r families; and 5) provide program and
60 procedural technical assistan ce and guidance to county health departments to facilitate service to eligible persons. Two additional goals are explicitly related to adherence: 1) to help patients adhere to their treatment regi mens and 2) to assist patients in avoiding interruption in ARV regimens (Florida Depa rtment of Health: Bureau of HIV/AIDS, 2003). Many of the standards associated with the Florida ADAP are consistent with current recommendations and guidelines found in the Department of Health and Human Services (DHHS) treatment guidelines (Na tional Institutes of Health, 2006). For example, clinical eligibility to start antiret roviral therapy with the Florida ADAP mirrors the recommendations of the DHHS for ini tiation of HAART. Similarly, the ADAP requires ongoing HIV RNA measurement and CD4 lymphocyte counts every three to four months to monitor response to treatment as recommended by the DHHS. Although the ADAP is primarily a medica tion access program, administrative functions incorporate actions to monitor and reinforce adherence to HAART. The Florida ADAP Program Manual addresses a number of issues related to HAART adherence which are summarized in Table 5.
61 Table 5 Adherence-Related Statements in the Florida ADAP Program Manual 1. Any department-specified or local hea lth department adherence policy and/or procedure must be followed in educati ng and counseling the patient about taking medications. 2. If there are problems with adherence, es pecially if a change in the HAART regimen is due to nonadherence, the patient's case manager and healthcare provider should be notified. 3. Patients in the Florida ADAP may be dise nrolled if the patient fails to pick up medications for more than 60 days and or is refusing to adhere to the medication regimen despite counseling and supports or other assistance offered. This decision should be made with the treating health care provider's input and guidance. 4. Patients are responsible for picking up thei r medications on time each month before they run out. 5. It is the goal of the ADAP to help patien ts adhere to their treatment regimens. 6. Patients have to cooperate in pick ing up medication and providing required information as requested or required. Adapted from Florida Department of Health: Bureau of HIV/AIDS. (2003). AIDS Drug Assistance Program Manual (ADAP). Tallahassee: Florida Department of Health. The Florida ADAP Program Manual also a ddresses patients who are nonadherent to HAART. Nonadherence is defined as not picking up HAART agents from the pharmacy within 35 days of the last pharmacy refill (Florida Department of Health: Bureau of HIV/AIDS, 2003). The statements in Table 6 summarize the process that ADAP staff is expected to follow when a nonadherent patient is identified.
62 Table 6 Florida ADAP Procedures Related to Nonadherence and Failure to Pick-up 1. If the patient is five or more workdays late for a scheduled medication pickup: An inquiry should be initiated to determine the reason for the delay in picking up medications. The case manager and treating healthcare provide r must be notified as soon as possible. A determination should be made as to whether or not the patient has had an interruption in drug therapy. If the patient last picked up a 30 day supply of medication, and has not been back to pick up for 35 or more days, then there has been an interruption in therapy. If there has been no interruption in drug therapy, the patient should be encouraged and assisted in getting his or her medications for the month. If there has been an interruption in drug therapy of five or more days, a consultation with the treating healthcare provider should be made as soon as possible before the patient is allowed to pick up his or her medications for the month. 2. Patients who report "borrowing" or using another patient's medications to continue their own treatment are still considered to have an interruptio n in therapy if medications were issued by the Department more than 35 days prior. Patients who report using "leftover" medications in their possession also may have been nonadherent. Patients should not be given medications until the healthcare provider has been consulted and has given approval to issue medications or other instructions. 3. If the patient fails to show at all for three weeks to 30 or more days to pick up medications, the treating healthcare provider and case manager must be notified. If the patient comes in for medication at this point, he or she must see the treating healthcare provider before being given medication. If the treating healthcare provider states that an office visit is not needed or desired, and wants medication issued, give the patient medications and document the name of the healthcare provider's staff who gave the instruction to issue the medications. 4. If the patient fails to show at all for 60 days or more, he or she should be closed out of the ADAP system. Notify the case manager and the healthcare provider that the patient has not picked up medication for 60 days prior to closure. If the patient shows up in 60 days and has not been closed, he or she must see the treating healthcare provider, have new labs, and obtain prescriptions. 5. If the patient has missed 90 days or more of medication, has not already been closed out, and comes in, no medications can be given. The patient must see the treating healthcare provider, provide new labs and obtain new prescriptions. Notify the healthcare prov ider and the case manager that the patient has missed 90 days of medication. 6. Documentation of contact with the patient and the healthcare provider must be placed in the patient record. Patients who decide to stop drug therapy without the knowledge or consent of their treating healthcare provider should be advised to contact him or her. Notice of therapy interruption should be given to the healthcare provider by the ADAP contact. Adapted from Florida Department of Health: Bureau of HIV/AIDS. (2003). AIDS Drug Assistance Program Manual (ADAP). Tallahassee: Florida Department of Health.
63 The Florida ADAP is unique with their appr oach to closely a nd regularly monitor medication refill adherence as part of the programs standard of practice. ADAP staff records the date that patients pick up their medications from the pharmacy in each patients record. The program provides a 30 day maximum supply of ARV medication. Prescriptions can be refilled 28-35 days after the previous prescription has been dispensed. Programmatic standards state that if the patient is five or more workdays late for a scheduled medication pickup, an inquiry should be initiated to determine the reason for the delay in picking up medications. The case manager and treating healthcare provider must be notified as soon as possible. A determination should be made as to whether or not the patient has had an interrupt ion in drug therapy whic h they define as a time lapse of 35 or more days since the pa tient last picked up a 30 day supply of medication (Florida Department of H ealth: Bureau of HIV/AIDS, 2003). If there has been no interruption in drug therapy, the patient is encouraged and assisted in getting his or her next supply of monthly medicat ions. If there has been an interruption in drug th erapy of five or more days, a consultation with the treating healthcare provider is made as soon as possible before the patient is allowed to pick up his or her medications for the month. The tr eating healthcare provider can either approve additional medication dispensing or hold furthe r medication dispensing. If dispensing is put on hold, the healthcare provider usually schedules a face-to-face meeting with the patient or requires that the pa tient schedule an appointment w ith the adherence specialist for additional assessment and intervention. It is this medication refill monitoring process that serves as the main monitoring component for the structured adherence intervention in this study.
64 Usual Care Usual care in this study included patients th at received their antiretroviral therapy from Florida Medicaid. Like the ADAP, this program also provided a 30 day supply of medication but did not contai n procedural or administra tive conditions which could impact adherence. Healthcare providers we re not informed of missed or late pharmacy refills. It is theoretically possible that a patient could fa il to pick-up any medication or could pick up medication re fills erratically without the prescribers knowledge. Medication Adherence Assessment Providers of the outpatient HIV trea tment program monitor self-reported medication adherence at each clinic visit for patients participating in the ADAP and usual care programs. During the routine clinic inta ke process, a medical assistant asks each patient several adherence related questions and documents responses on a clinic-designed Medication Adherence Assessmen t Form. Although no validity or reliability testing has been performed on this specific assessment t ool, self-reported adherence based on patient recall of the number of doses missed in the last 7-30 days has been reported in the literature as a valid indicator of adhe rence (Haubrich et al., 1999; Mannheimer, Friedland, Matts, Child, & Chesney, 2002; Mo ntaner et al., 1998; Nieuwkerk & Oort, 2005). These self-report adherence questions are listed in Table 7.
65 Table 7 Medication Adherence Assessment Questions (Self-Report) 1. How many doses of your HIV medication have you missed in the last week (7 days)? 2. How many doses of your HIV medication have you missed in the last month (30 days)? 3. Are you having any side effects from your HI V medications that interfere with your ability to take them on a regular basis? Using the patients response to the medi cation adherence asse ssment, the provider calculated an adherence rate fo r each patient. The monthly a dherence rate was calculated as: (1 [missed doses in the last 30 days / prescribed doses in the last 30 days] ) X 100%. This percentage was documented in the patients medical record and was subsequently entered into the LabTacker database by a data entry assistant. Adherence Services and Interventions The outpatient HIV treatment program employs a registered nurse in the capacity of adherence specialist. The adherence specialist was avai lable to all patients that received care at the outpatient HIV treat ment program including ADAP and usual care patients. While most patients are referred to the adherence specialist from their healthcare provider, patients can also self-re fer to the specialist for assistance. ADAP staff also refers patients to the adherence sp ecialist when they identify a perceived need for adherence assessment or in tervention. Typical services and interventions provided by the adherence specialist are summarized in Table 8.
66 Table 8 Services and Interventions Provid ed by the Adherence Specialist General education related to the HIV disease proc ess, HIV treatment medications and goals of treatment General and specific information related to ARV medications including dosing, timing, potential side effects and side effect management Medication scheduling assistance Education related to the importance of adheren ce, methods to prevent the development of ARV resistance, and pharmacy and medication refill processes Assessment of support systems Identification of potential barriers to adherence Support and counseling Prescription of adherence aids: pill boxes, timers, alarm watches Adherence interventions ma y include the recommendation to use a pill box, the use of a programmable wristwatch which can display multiple digital messages to serve as reminders throughout the day, reminder te lephone calls, education, counseling, and support. All services and mate rials are provided free of charge to the patient as they are provided by Ryan White Gran t funding and donations. Documentation of adherence assessment and intervention is documented in the clinic medical record along with the length of the visit in increments of 15 minute sessions. The adherence speci alist maintains a Microsoft Access database containing the patients self-reported adherence per centage, number of visits for adherence counseling, length of time associ ated with each consultation, and interventions or aids that were provided or recommended to the patient.
67 Summary The Florida ADAP includes a unique stru ctured adherence intervention as a standard component within their medication access program. Pharmacy refill data is closely monitored by ADAP staff with the intent that patient s will refill their medication on time, month after month. After picking up a months supply of HAART medications, patients have approximately a one-week pe riod to refill their next months supply beginning at day 28 and ending at day 35. ADAP staff is in close contact with pharmacy, medical, and nursing staff to keep everyone pr oactively informed of patients that may have adherence deficits. Patients who do not pick up monthly refills within the appropriate timeframe are required to consult with the healthcare provider who may grant permission to resume medication or may require the patient to consult with the adherence specialist for further assessment and possible intervention. This structured adherence process served as the primary inte rvention in this research study. Research Design Study Design This study used a retrospective comparativ e design to analyze secondary data. The study was designed to better understand the effects of a structured adherence intervention associated with an existi ng medication access program on adherence to HAART and response to HAART treatment compared to usual care. In the SAI group, providers closely monitored monthly HAART medication refills and provided structured adherence intervention when indicated. Patient s in the usual care group were enrolled in a Medicaid-funded medication access program and did not receive ongoing medication refill monitoring and structured adherence intervention.
68 Study Population The study population included a ll eligible patients participating in the Florida ADAP or in a Medicaid-funded medicati on access program who received HAART medications and outpatient HI V medical care from one single treatment center and pharmacy in west central Florida during the calendar year 2005. This time period was selected to minimize the influence of the im plementation of Medicare Part D prescription medication coverage which was initiated in January 2006. The implementation of Medicare Part D prescripti on drug coverage had the poten tial to introduce additional confounders as the process was complicated for patients and staff, and resulted in difficulty in accessing medica tion for many individuals. Inclusion Criteria Included were all patients 18 years of ag e or older who completed a minimum of six consecutive months in the SAI or usual ca re program as the sole means of obtaining HAART medications during ca lendar year 2005 while on a consistent HAART regimen. All patients received their medication from the single pharmacy associated with the HIV treatment center. Exclusion Criteria Exclusion criteria included less than six m onths of consecutive pa rticipation in the SAI program or usual care program, alterati ons to the ARV regimen during the six-month period, or use of a pharmacy ot her than the on-site pharmacy.
69 Sample Size This study used secondary data and the sample size was fixed. A preliminary query of the database suggested there were 1,355 potential subjects eligible for evaluation. Of these, 37% composed the us ual care group while 63 % composed the SAI group. Since the exact number of patients meeting the inclusion criteria was not initially known, a conservative estimate of 50% (n= 678) was considered for the purpose of establishing a power analysis. Results of th e analysis using this conservative estimate should ensure that there is adequate power to conduct the proposed analyses. Results of the power analysis for more liberal estimates of 60% and 70% of the entire population are also provided to demonstrate the increased power available for the study should these situations be found in the data. Power Analysis Power estimates were derived for multiv ariable logistic regression, the least powerful and most complex of the analyses proposed in this study, thereby ensuring adequate sample size for all of the anal yses in the study. Table 9 summarizes the estimated range of possible subjects and the re spective power analysis associated with the estimate. Power analyses were also conducte d using inclusion estimates of both 60% and 70%. The power estimates are displayed in Table 9.
70 Table 9 Power Estimates Based on Projected Sample Population Database Population (N) Percentage of Database Meeting Inclusion Criteria Sample Size (N) Power 1,355 50% 678 0.80 1,355 60% 813 0.86 1,355 70% 949 0.91 Because of the unique nature of this st udy, no data were found in the literature to suggest an appropriate effect size for this st udy. Therefore, effect size was chosen based on programmatically relevant changes. For th is analysis a 10% improvement response to treatment by patients in the SAI program comp ared to those in the usual care program was identified as being programmatically relevant. Based on this assumption, a minimum sample size of 678 patients achie ves a power of .80 at the .05 level of significance when expecting a .10 effect. Th e power analysis was conducted using the Power Analysis and Sample Size (PASS) statistical prog ram (Hintz, 2001). Setting All patients considered for this study we re enrolled in a comprehensive outpatient HIV care program in west central Florida. This center was establ ished in 1989 and is the largest single public provider of HIV care on the west coast of Florida. The center serves approximately 1500 active patients. The clin ic provides multiple services including medical and nursing care for patients with HIV disease along with pharmacy, dental, and social services.
71 Most patients were seen by their HIV car e provider every one to three months. Most patients obtained their medications at the on-site pharmac y. All medications dispensed from the pharmacy were limited to a 30-day supply requiring prescriptions to be refilled on a monthly basis. Study Variables Dependent Variables There are two outcome variables in this study: adhere nce (self-reported medication adherence and pharmacy refill adherence) and treatment response (CD4 lymphocyte response and HIV RNA response). Although adherence could be considered a proximal outcome variable that influences treatment response, it was considered as a terminal outcome in this study. Self-reported medication adherence. Satisfactory adherence is defined as 90% or more of the pills prescribed in any regimen taken in accordance with the prescription plan. This is in agreement with the pr ocedure from several other HIV medication adherence studies (Gordillo, del Amo, Sori ano, & Gonzalez-Lahoz, 1999; Gross, Bilker, Friedman, & Strom, 2001). All self-reported adherence levels collected during the study period were assessed. Pharmacy refill adherence. Pharmacy prescription refill data is used as a surrogate for medication-taking behavior and t ypically compares actual versus expected refills. Although this method does not guarant ee that the medications were ingested, it does represent maximum probable adherence. Refill adherence is calculated as the percentage of times the index ARV agent (p rotease inhibitor or non-nucleoside reverse
72 transcriptase inhibitor) was refilled by the pharmacy within the 28-35 day timeframe during the study period. CD4 lymphocyte response. CD4 lymphocytes were meas ured by four commercial laboratories using flow cytometry and hematology analyzers using fresh blood specimens. Test results indicated the numbe r of CD4 cells per cubic millimeter of blood (Bartlett & Gallant, 2005). Im munologic response to HAART treatment was measured as the change in CD4 lymphocyte count from base line to 6 months. A stable or increasing CD4 lymphocyte count is representa tive of successful HAART treatment. HIV RNA response. Quantitative HIV RNA levels were measured by four commercial laboratories. Test results indica ted the number of copies of HIV RNA per 1 mL of plasma (Bartlett & Gallant, 2005). Virologic response to HAART treatment was measured as the change in HIV RNA level from baseline to 6 months An undetectable or decreasing HIV RNA level is representa tive of successful HAART treatment. Independent Variables: Treatment Conditions Group membership (SAI versus usual care) is the independent variable of interest in this study. The usual care group for this study included patient s using Medicaid to fund their HAART at the same on-site clinic pharmacy. The Medicaid program did not include a specific adherence or prescription refill monito ring component. A maximum 30-day supply of medication was dispensed by the pharmacy at any one time. The SAI includes a number of standard procedures to monitor and strengthen adherence as described earlier.
73 Independent Variables: Covariates Covariates include adherence services and intervention, ARV specific factors, self-reported medication adherence, pharmacy refill adherence, socio-demographic factors, and HIV disease specific factors. Adherence services and intervention. The number of face-to-face visits with the adherence specialist during the study period, total length of time (in minutes) associated with these face-to-face visits, and prescrip tion of adherence aids were assessed to compare the utilization of the adherence sp ecialists services between the groups. ARV specific factors. Characteristics related to the HAART regimen were assessed including dosing frequency (once vs. twice daily), type of regimen based on index agent (non-nucleoside reve rse transcriptase-based vs. pr otease inhibitor-based), and pill burden defined as the number of HAART pills prescribed per day. Sociodemographic factors. Sociodemographic variable s included age, gender, race, ethnicity, income level, housin g status, and health insurance. HIV disease specific factors. These clinical covariates included risk factor for HIV transmission, the number of years diagnos ed with HIV infecti on, stage of disease (HIV vs. AIDS), and the presence of comorbid conditions known to impact HAART adherence including the presen ce of active substance abus e and active mental health disorders (depression, bipolar disord er and anxiety disorder). Tables 10 and 11 list all va riables considered in this study with detailed information related to the source of the da ta, frequency of measurement, operational definition and level of measurement.
74 Table 10 Variables, Definitions and Measurement (Part I) Source of Data FrequencyOperational Definition / Measurement Level of Measurement Dependent Variables Medication Adherence: Self Report LabTracker Month 0, 3 and 6 Self-reported number of ARV medication doses missed in the last 7 days. Adherence calculated as: (1 [missed doses / prescribed doses]) X 100%. Average adherence >/= 90% = 1; <90% = 0. Nominal Medication Adherence: Pharmacy Refill Pharmacy administrative database Month 1, 2, 3, 4, 5, 6 Percentage of times the index ARV medication (PI or NNRTI) was refilled by the pharmacy within the 28-33 day timeframe during the sixmonth study period. Average adherence >/= 90% = 1; <90% = 0. Nominal Immunologic Response: CD4 Lymphocyte LabTracker Month 0, 6 Change in value from month 0-6. No change or increase = 1; Decrease = 0. Nominal Virologic Response: HIV RNA LabTracker Month 0, 6 Change in value from month 0-6. No change or decrease = 1; Increase = 0. Nominal Independent Variables Treatment Condition: Group Membership Structured Adherence Intervention or Usual Care Nominal Covariates Sociodemographic Factors Gender LabTracker Baseline Male, Female, Transgender Nominal Age LabTracker Start of study period Age in years Continuous Race LabTracker Baseline White, Black, Asian/Pacific Islander, Native American (Alaskan), Multiple Nominal Ethnicity LabTracker Baseline Hispanic, Nominal Income level LabTracker Baseline < 100% FPL, 101-200% FPL, 201-300% FPL, >300% FPL Ordinal Housing Status LabTracker Baseline Permanent, Nonpermanent Nominal Health Insurance LabTracker Each encounter None, Medicaid, Medicare, Medicaid and Medicare, Hillsborough HealthCare Nominal
75 Table 11 Variables, Definitions and Measurement (Part II) Source of Data FrequencyOperational Definition / Measurement Level of Measurement HIV Disease-Specific Factors HIV Risk Factor LabTracker Baseline MSM, Heterosexual, IDU, MSM and IDU, Tissue/Blood Transfusion, Hemophilia, Perinatal, Unknown Nominal Disease Stage LabTracker Baseline Number of years the subject has been living with HIV Disease calculated as length of time from the first HIV-positive antibody test to the date of study entry Interval HIV Disease Status LabTracker Once (study entry) HIV or AIDS Nominal Active substance abuse LabTracker Month 1,2,3,4,5,6 Yes, No Nominal Presence of MH disorder LabTracker Month 1,2,3,4,5,6 Yes, No Nominal ARV Specific Factors Dosing frequency LabTracker Baseline One daily, twice daily Nominal Type of regimen LabTracker Baseline PI based, NNRTI based Nominal Daily pill burden LabTracker Baseline Number of total ARV pills taken per day Continuous Adherence Services and Intervention Number of Face-to-face adherence counseling visits Adherence database Month 1,2,3,4,5,6 Number of face-to-face visits with Adherence Specialist during study period. Continuous Time associated with face-to-face adherence counseling Adherence database Month 1,2,3,4,5,6 Total time in minutes of face-toface visits with Adherence Specialist Continuous Adherence aids prescribed Adherence database Month 1,2,3,4,5,6 Yes/No Nominal
76 Data Sources Data were obtained from several electroni c databases. This section will review the data sources, validity of data, methods a ssociated with the pro cess of obtaining deidentified data, data management, and security. LabTracker (Ground Zero Software, 2007) software has been used as the primary database for clinical and admi nistrative data for over four years. Sociodemographic data are reassessed at the patients first outpatient visit in each calendar year by an advanced registered nurse practitioner or a physic ian. These data are subsequently entered and updated in the La bTracker system by two dedicated data entry assistants. Validity of data is conti nually monitored by a regi stered nurse and an advanced registered nurse practitioner w ho compare Lab Tracker data to medical record data and laboratory report forms to a ssure congruence. Accuracy of data is externally audited twice a y ear by two independent agencies to assure accuracy of recorded data and has consistently been 97-100% accurate when compared to medical record data and laboratory reports. Pharmacy data was stored in the Pharmacy Management System (PMS) (Etreby Computer Company, 2007). Data was entere d by clinical pharmacists and pharmacy technicians and was verified by the supervising pharmacist on an ongoing basis. Data is externally audited for accurate ness annually and has consistently been 95-100% accurate. A Microsoft Access database contained the a dherence specialis ts utilization data associated with each patient. Data was entered by a registered nurse working in the capacity as an adherence specia list. Accuracy of the data is externally audited once a
77 year by an independent agency to assess accura cy of recorded data and has consistently been 98-100% accurate when compared to medical record data. Data Collection Procedures The health center administra tor (HCA) at the clinical f acility had complete access to the LabTracker, Pharmacy Management System, and adherence specialist data bases. Information from all three data base s was initially linked by a four digit unique internal identification number that is used throughout the c linical facility. Once the HCA matched all data in to one Microsoft Excel file, the unique inte rnal client identifier was replaced with a randomly generated study c ode using Microsoft Ex cels random number generating program. Once the files were matched, the randomly assigned study code number was assigned as the only means of identification, and the matching algorithm was destroyed by the HCA. The HCA provided the invest igator with a Microsoft Excel file containing all data elements and variables identified in this study. The resulting data file had no direct or indirect links that could id entify any individual participan t or group of participants. Procedures Institutional Review Boards Approvals for Institutiona l Review Board (IRB) exem ption were obtained from the University of South Floridas Office of Research, Division of Research Compliance IRB (Appendix A) and the Florida Department of Health IRB (Appendix B). Exemption from the IRB was granted because the study used existing data, documents, and records that were recorded without identifiers.
78 Letter of Support A letter indicating support for the study was obtained from the director of the clinical facility. Data Management The Statistical Package for the Social Sc iences (SPSS) version 15.0 was used for data analysis and data management. The data files were housed on a dual-passwordprotected network server at the clinical site with acce ss only by the researcher and research assistant. Missing Data Incomplete or missing sociodemographi c information, HIV disease specific factors, and ARV specific fact ors are highly unlikely as thes e elements are mandatory in the LabTracker database. While missing labo ratory data is also unlikely, missing CD4 lymphocyte counts and HIV RNA levels will be imputed using the mean value of a subjects laboratory data co llected in the study period. Data Analysis Plan The effects of the SAI will be assessed by testing two hypotheses. The following section describes the data analyses methods. Hypothesis 1 : Patients participating in the SAI will have higher levels of selfreported and pharmacy refill adherence compared to patients receiving usual care. Hypothesis 2 : Patients participating in the SAI program will have better immunologic (CD4 lymphocyte) and virolo gic (HIV RNA) responses to HAART compared to those receiving usual care.
79 Frequency distributions and descriptive statistics on a ll variables were performed to describe the study sample. There were tw o outcome variables in the study: adherence and treatment response. A series of bivariate analyses were conducted to investig ate the relationship between self-reported adheren ce and pharmacy refill adherenc e, group membership (SAI vs. usual care) and the covari ates in the study. Variables found to be independently associated with adherence were considered for inclusion in a regression model. Logistic regression was performed on the outcome variab les to test the effects of the treatment condition while controlling for adherence serv ices and intervention, HIV disease specific factors, ARV specific factors and sociodemographic factors. A series of bivariate analyses were conducted to investig ate the relationship between CD4 lymphocyte response and HIV RN A response, treatment conditions (SAI vs. usual care) and the covari ates in the study. Logistic regression was performed on CD4 lymphocyte response and HIV RNA respons e to test the effects of the treatment condition while controlling for HIV disease sp ecific factors, ARV specific factors and sociodemographic factors. Results of the study were reported as group data and no identifying information related to any person is presented. Summary Chapter 3 described the operating standa rds within the Florida ADAP that serve as a structured adherence intervention fo r patients receiving HAART and the Medicaid program that serves as the usual care group. The adherence assessment and intervention
80 processes, study design, population, setting, va riables and data collection procedures were described. Finally, the data analysis plan was summarized.
81 CHAPTER 4: FINDINGS This chapter presents the results of th is study. Following a description of the sample and comparison of the study groups, th e results of the biva riate and logistic regression analyses are reported. Study Sample The initial query of the LabTracker data base suggested 1,355 potential subjects eligible for analysis. After inclusion and ex clusion criteria were applied, 424 subjects were eligible for analyses. The SAI group included 204 subjects (48%) while 220 subjects (52%) in the us ual care group were elig ible for inclusion. Subjects Excluded from Analysis A total of 931 subjects did not meet incl usion criteria for this study. This number was higher than expected. The reasons for ex cluding these subjects are shown in Table 12. The primary reason for exclusion was not using the on-site pharmacy for ARV medication access. Patients with Medicaid or commercial insurance could select any community pharmacy to obtain medication. Alth ough the usual care group in this study also had the potential to use any community pharmacy, subjects elected to use the on-site pharmacy at the study site.
82 Table 12 Subjects Excluded from Study Reason for Exclusion Number Excluded (%) Not receiving antiretroviral therapy from the on-site pharmacy 565 (60.7) Not prescribed antiretroviral therapy for at least 6 consecutive months 291 (31.2) Missing data 49 (5.3) Death during the first se ven months of year 2005 26 (2.8) Note : N = 931 In theory, these two popul ations should be similar with regard to sociodemographic, HIV disease characteristics a nd comorbid conditions. Sociodemographic variables were available fo r 547 of 565 patients who did not use the on-site pharmacy. These variables were examined using Pearson Chi-Square analysis to identify differences between the usual care group of subjects and the subjects excluded from the study because they did not use the on-site pharm acy. No significant differences were found between or among the two groups. This information is displayed in Table 13.
83 Table 13 Comparison of Sociodemographic Characteri stics of SAI Group, Usual Care Group and Subjects Not Meeting Inclusion Criteria SAI Usual Care Excluded from Study Total n=204 Frequency (%) n=220 Frequency (%) n=547 Frequency (%) N971 Frequency (%) Gender Male 152 (74.5) 147(66.8) 366(66.9) 665 (68.5) Female 52 (25.5) 73(33.2) 181(33.1) 306 (31.5) Race White 123 (60.3) 106(48.2) 264(48.3) 493 (50.8) Black 76 (37.3) 110(50.0) 272(49.7) 458 (47.2) Other 5 (2.5) 4(1.8) 11(2.0) 20 (2.1) Ethnicity Non-Hispanic 144 (70.6) 165(75.0) 414(75.7) 723 (74.5) Hispanic 60 (29.4) 55(25.0) 133(24.3) 248 (25.5) Age at time of study (years) 18-29 22 (10.8) 9(4.1) 24(4.4) 55 (5.7) 30-39 52 (25.5) 42(19.1) 107(19.6) 201 (20.7) 40-49 79 (38.7) 99(45.0) 245(44.8) 423 (43.6) 50-59 46 (22.5) 52(23.6) 128(23.4) 226 (23.3) > 60 5 (2.5) 18(8.2) 43(7.9) 66 (6.8) Health Insurance Yes 69 (33.8) 219(99.5) 539(98.5) 827 (85.2) No 135 (66.2) 1(0.5) 9(1.6) 145 (14.9) Income Level (%FPL) <100% 124 (60.8) 168(76.4) 419(76.6) 711 (73.2) 101-200% 55 (27.0) 46(20.9) 111(20.3) 212 (21.8) >200% 25 (12.3) 6(2.7) 17(3.1) 48 (4.9) Housing Status Permanent 201 (98.5) 215(97.7) 534(97.6) 950 (97.8) Nonpermanent 3 (1.5) 5 (2.3) 13 (2.4) 21 (2.2) HIV disease specific characteristics and co morbid conditions were also compared between the subjects in the usual care gr oup and the subjects excluded from the study.
84 These variables were examined using Pearson Chi-Square analysis to identify differences between the usual care group of subjects and the subjects excluded from the study because they did not use the on-site pharm acy. No significant differences were found between the two groups. Table 14 di splays this information. Table 14 HIV Disease and Comorbid Conditions Characteristics of SAI Group, Usual Care Group and Subjects Not Meeti ng Inclusion Criteria SAI Usual Care Excluded Total n=204 Frequency (%) n=220 Frequency (%) n=547 Frequency (%) n=971 Frequency (%)HIV Disease Status HIV (nonAIDS) 87 (42.6) 68(30.9) 172(31.4) 327 (33.7) AIDS 117 (57.4) 152(69.1) 375(68.6) 644 (66.3) HIV Risk Factor MSM 85 (41.7) 61(27.7) 152(27.8) 298 (30.7) Heterosexual 106 (52.0) 127( 57.7) 319(58.3) 552 (56.8) IDU 10 (4.9) 26(11.8) 63(11.5) 99 (10.2) Other 3 (1.5) 6(2.7) 13(2.4) 22 (2.3) Presence of Active Substance Abuse Yes 18 (8.8) 25(11.4) 67(12.2) 110 (11.3) No 186 (91.2) 195(88.6) 480(87.8) 861 (88.7) Presence of Active MH Disorder Yes 54 (26.5) 65(29.5) 162(29.6) 281 (28.9) No 150 (73.5) 155(70.5) 385(70.4) 690 (71.1) In summary, the sociodem ographic, HIV disease, and comorbid characteristics between the usual care group and the group ex cluded from the study were approximately
85 equivalent. Chi-square test s were performed to look for significant differences in characteristics between these groups. No significant differences were found. Characteristics of the Study Sample The mean age of the sample was 44.3 years with a range of 19-76 years ( SD : 9.36 years). The majority of s ubjects in the sample were men (n=299; 70.5%) and selfidentified as non-Hispanic (n=309, 72.9%). The majority of the subjects were white (n=229, 54%) and 186 (43.9%) were black. The pr imary risk factors associated with HIV infection for the sample were (a) hetero sexual (n=233, 55%); (b) men having sex with men (n=146, 34.4%); and (c) in jection drug use (n=36, 8.5%). The mean time living with HIV disease was 7.6 years with a range of 125 years. The most common ARV regimen in the sample was protease inhibitor based (n=235; 55.4%). Subjects had a daily pill burden range from 2 to 15 pills per day with a mean daily pill burden of 5. The SAI group included higher percenta ges of both younger patients (19-29 years) and patients in the 5059 year age range. These diffe rences were significant (Chisquare 15.897, df 4, p=.003). A higher percentage of white patients were seen in the SAI group while the usual care group included a highe r percentage of black subjects. These differences were signifi cant (Chi square 6.994; df 2, p=.03). Table 15 displays the frequency and percent for the demographic fact ors (gender, race, et hnicity, and age) of the patients who met inclusion criteria.
86 Table 15 Sociodemographic Composition of the Study Groups Characteristics SAI n = 204 Frequency (percent) Usual Care n = 220 Frequency (percent) Total N = 424 Frequency (percent) Gender Male Female 152 52 (74.5) (25.5) 147 73 (66.8) (33.2) 299 125 (70.5) (29.5) Race White Black Other 123 76 5 (60.3) (37.3) (2.5) 106 110 4 (48.2) (50.0) (1.8) 229 186 9 (54.0) (43.9) (2.1) Ethnicity Non-Hispanic Hispanic 144 60 (70.6) (29.4) 165 55 (75) (25) 309 115 (72.9) (27.1) Age at time of study (years)** 19-29 30-39 40-49 50-59 > 60 22 52 79 46 5 (10.8) (25.5) (38.7) (22.5) (2.5) 9 42 99 52 18 (4.1) (19.1) (45.0) (3.6) (8.2) 31 94 179 98 23 (7.3) (22.2) (42.0) (23.1) (5.4) Note. *=p<.05; **=p<.01 Preliminary analyses demonstrated that several covariates ha d small cell sizes. Consequently several covariates were coll apsed into fewer categories to provide the reader with more useful information related to the population. Age of the participants was collapsed from a continuous variable to an ordinal variable with 5 age groups. Income levels were reduced from four groups to three due to small sample size in the upper income range. Three male transgender patients were grouped as male. Several HIV risk factors were grouped as other due to small cell sizes. Finally, the number of years living with HIV disease wa s collapsed into four groups.
87 Health and Income Related Characteristics Table 16 displays the frequency for housing factors, health insurance, income, and HIV disease specific information. As expect ed, a higher percentage of subjects in the usual care group had health insurance while subjects in the SAI relied on the ADAP to fund their medication. These differences were significant (Chi-Square 109.849, df 1, p<.0005). More subjects in the usual care group had lo wer income (<100 FPL) while a higher percentage of subjects in the SA I had higher income (>200% FPL). These differences were also si gnificant (Chi-Square 18.5, df 2, p<.0005). There was a higher percentage of subjects with a risk factor of MSM in the SAI and a higher percentage of IDUs in the usual care group (Chi-square 13.364, df 3, p=.004). Lastly, there was a higher percentage of patients with an AIDS diagnosis in the usual care group (Chi-Square 6.288, df 1, p=.012). Substance Abuse and Mental Health Disorders Table 17 displays the frequency and per centage of patients diagnosed as having an active substance abuse problem or a ment al health disorder (depression, bipolar disorder, or anxiety). The SAI group and usual care group were approximately equivalent. A total of 43 patients (10.1%) were identified as having an active substance abuse problem while 119 (28.1%) were diagnos ed as having mental health disorder.
88 Table 16 Health and Income Related Char acteristics of the Study Group Characteristics SAI n = 204 Frequency (percent) Usual Care n = 220 Frequency (percent) Total N = 424 Frequency (percent) Housing Status Permanent Nonpermanent 201 3 (98.5) (1.5) 215 5 (97.7) (2.3) 416 8 (98.1) (1.9) HIV Risk Factor** MSM Heterosexual IDU Other 85 106 10 3 (41.6) (52.0) (4.9) (1.5) 61 127 26 6 (27.8) (57.7) (11.8) (2.7) 146 233 36 9 (34.4) (55.0) (8.5) (2.1) Years Living with HIV < 5 6-10 11-15 >15 88 55 36 25 (43.1) (27.0) (17.6) (12.3) 89 73 41 17 (40.5) (33.2) (18.6) (7.7) 177 128 77 42 (42.7) (30.2) (18.2) (9.9) Income Level *** (% Federal Poverty Level) <100 % 101-200 % > 200 % 124 55 25 (60.8) (27.0) (12.2) 168 46 6 (76.3) (21.0) (2.7) 292 101 31 (68.9) (23.8) (7.3) Health Insurance*** Yes No 69 135 (33.8) (66.2) 219 1 (99.5) (0.5) 288 136 (67.9) (32.1) HIV Disease Status* HIV (non-AIDS) AIDS 87 117 (42.6) (57.4) 68 152 (30.9) (69.1) 155 269 (36.6) (63.4) Note. *=p<.05; **=p<.01; ***p<.001
89 Table 17 Substance Abuse and Mental Health Disorders in Study Group Characteristics SAI n = 204 Frequency (percent) Usual Care n = 220 Frequency (percent) Total N = 424 Frequency (percent) History of Active Substance Abuse Yes No 18 186 (8.8) (91.2) 25 195 (11.4) (88.6) 43 381 (10.1) (89.9) Presence of Mental Health Disorder Yes No 54 150 (26.5) (73.5) 65 155 (29.5) (70.5) 119 305 (28.1) (71.9) ARV Therapy Characteristics Table 18 describes the use of antiretrovi ral therapy associated with the study population. There was a significan t difference between the groups related to the type of ARV regimen subjects received (Chi-Square: 7.672, df 2, p=.022). More than half of the patients in each group receive d a protease inhibitor-based regimen. Approximately 6% of all patients received a non-nucleoside re verse transcriptase inhibitor (NNRTI) based regimen during the study period with a higher percentage in the SAI group. Based on the DHHS guidelines in place during 2005, triple -NRTI regimens were not recommended regimens (National Institutes of Health, 2006). The mean daily pill burden was 5.4 pills per day with a range of 2-15 pills per day. Over two-thirds of all patients received a regimen that required twice-daily dosing.
90 Table 18 Use of Antiretroviral Medi cations in Study Groups Characteristics SAI n = 204 Frequency (percent) Usual Care n = 220 Frequency (percent) Total N = 424 Frequency (percent) Type of Regimen* Protease Inhibitor NNRTI Triple NRTI 112 74 18 (54.9) (36.3) (8.8) 123 91 6 (55.9) (41.4) (2.7) 235 165 24 (55.4) (38.9) (5.7) Dosing Frequency Once Daily Twice Daily 59 145 (28.9) (71.1) 73 147 ` (33.2) (66.8) 132 292 (31.1) (68.9) Daily ARV Pill Burden 2-4 5-8 9-15 89 99 16 (43.6) (48.5) (7.8) 84 105 31 (38.2) (47.7) (14.1) 173 204 47 (40.8) (48.1) (11.1) Note. *=p<.05 Adherence Services and Intervention The majority of the study populatio n (92.7%) did not receive adherence counseling or intervention (n=393). A total of 31 patients (7.3%) received at least one face-to-face counseling session with the adhe rence specialist. Subjects received anywhere from one session to 23 sessions with a range of total c ounseling time from 30 minutes to 1,230 minutes. One outlier receive d 23 sessions with a cumulative counseling time of 1,230 minutes. With this outlier rem oved from analysis, the range of counseling time was 30-360 minutes with a mean of 91 mi nutes. Fewer than 4% of all patients received adherence aids such as pill boxes, customized medication schedules, and alarm watches (n=16). There were no significant differences related to adherence services and
91 interventions between the SAI a nd usual care group. Table 19 displays the frequency and percentages of adherence couns eling services and interventi ons received by subjects. Table 19 Adherence Services and Intervention Adherence Measures SAI n = 204 Frequency (percent) Usual Care n = 220 Frequency (percent) Total N = 424 Frequency (percent) Received Adherence Counseling Session Yes No 16 188 (7.8) (92.2) 15 205 (6.8) (93.2) 31 393 7.3 92.7 Adherence Counseling: (Minutes) 0 30-60 61-120 >120 188 5 9 2 (92.2) (2.5) (4.4) (1.0) 205 3 7 5 (93.2) (1.4) (3.2) (2.3) 393 8 16 7 (92.7) (1.9) (3.8) (1.7) Number of Face-to-Face Adherence Counseling Sessions (per patient) 0 1 2 3 5 >5 188 12 3 1 0 0 (92.2) (5.9) (1.5) (0.5) 0 0 205 11 1 1 1 1 (93.2) ( 5.0) (0.5) (0.5) (0.5) (0.5) 393 23 4 2 1 1 (92.7) (5.4) (0.9) (0.5) (0.2) (0.2) Adherence Aids Prescribed Yes No 6 198 ( 2.9) (97.1) 10 210 (4.5) (95.5) 16 408 (3.8) (96.2)
92 Self-Reported and Pharmacy Refill Adherence Characteristics related to self-reported adherence and pharmacy refill adherence are summarized in Table 20. Three hundred sixty-four patients (85.8%) self-reported medication adherence of at least 90% while 60 patients (14.2%) repo rted adherence rates less than 90%. A higher percentage of subjects self -reported adherence rates > 90% in the SAI group. There were significant differences between the two groups (Chisquare:19.581, df 1, p<.0005). Overall, adherence levels of at least 90% as measured by pharmacy refill pick-up were lower than the self-report measurements A greater percentage of patients in the SAI group had pharmacy re fill adherence rates > 90%. These differences were significant (Chi-Square: 7.578, df 1, p=.006). Table 20 Self-Reported Adherence and Pharmacy Refill Adherence Adherence Measures SAI Frequency (percent) n = 204 Usual Care Frequency (percent) n = 220 Total Frequency (percent) N = 424 Self-Report Adherence*** > 90% < 90% 191 13 (93.6) (6.4) 173 47 (78.6) (21.4) 364 60 (85.8) (14.2) Pharmacy Refill Adherence** > 90% < 90% 120 84 (58.8) (41.2) 100 120 (45.5) (54.5) 220 204 (51.9) (48.1) Note. **=p< .01; ***p<.001
93 Treatment Response The majority of subjects demonstrated a favorable (stable or increasing) CD4 lymphocyte response (63.9%). A higher percen tage of these subject s were seen in the SAI group but this was not a statistically si gnificant finding. Howe ver, a significantly higher percentage of patients in the SAI (79.4%) demonstrated a favorable virologic response (stable or declining HIV RNA level) compared with 45.9% in the usual care group (Chi-square 50.442, df 1, p<.0005). Table 21 depicts this information. Table 21 Treatment Response by Group Membership Adherence Measures SAI Frequency (percent) n = 204 Usual Care Frequency (percent) n = 220 Total Frequency (percent) N = 424 CD4 Lymphocyte Count Stable or Increasing Decreasing 139 65 (68.1) (31.9) 132 88 (60.0) (40.0) 271 153 (63.9) (36.1) HIV RNA Response*** Stable or Declining Increasing 162 42 (79.4) (20.6) 101 119 (45.9) (54.1) 263 161 (62.0) (38.0) Note. ***p<.001 Summary Bivariate and descriptive an alyses related to the study groups and covariates have been presented. Several statistically signi ficant differences between the SAI and usual care group have been reported.
94 Bivariate Analyses and Logistic Regression The following section will describe the biva riate analyses and logistic regression associated with the study. Initially a series of Chi-Square analyses were conducted to investigate the relationship between adhere nce outcomes (self-reported and pharmacy refill), treatment response (CD4 lymphocyt e and HIV RNA), treatment conditions (SAI and usual care) and the covari ates in the study. Variables found to be independently associated with adherence or treatment res ponse at a significance le vel of p = .10 or less were considered for inclus ion in a regression model. Logistic regression was performed on each outcome variable (self-reported adherence, pharmacy refill adherence, CD4 lymphocyte response and HIV RNA response) to test the effects of the treatm ent condition while controlling for potentially confounding effects (adherence se rvices and intervention, HIV disease specific factors, ARV specific factors and soci odemographic factors). All models were checked for high interc orrelation using collinearity diagnostics within SPSS. Tolerance values were satisfa ctory with no evidence of intercorrelation between covariates. Singularity was asse ssed using SPSS. Variance inflation factor values were all greater than 10 suggesting no evidence of singularit y. Omnibus tests of model coefficients were performed on each of the models and values <.05 were obtained. These findings suggest acceptable goodness of fit for the models. Hosmer and Lemeshow tests were performed for each of th e models and values >.05 were calculated. These findings also suggest support of the models.
95 Study Aim One: Adherence Outcomes The purpose of the first study aim was to determine whether patients participating in the SAI program experienced higher le vels of adherence co mpared to patients receiving usual care, control ling for adherence services and intervention, HIV diseasespecific factors, ARV-specific factors, a nd sociodemographic factors. Two hypotheses were tested: 1) Patients participating in the SAI program will have higher levels of selfreported adherence compared to patien ts receiving usual care, controlling for selected covariates. 2) Patients participating in the SAI program will have higher levels of pharmacy refill adherence compared to patients receiving usual care, controlling for selected covariates. Self-Reported Adherence Bivariate analyses were calculated on self-reported adherence and each of the covariates and the treatment condition. The data are displayed in Tables 22 and 23. There were several significant findings. A pproximately 65% of patients who had health insurance self-reported adheren ce rates of at least 90% while a higher percentage (81.7%) of subjects without insurance se lf-reported adherence levels > 90%. Individuals who reported acquisition of HIV inf ection associated with intr avenous drug use (IDU) route had lower self-reported medication adherenc e while the men who have sex with men (MSM) group reported higher levels. A higher pe rcentage of subjects that did not have a history of active substan ce abuse (91.2%) self-repor ted adherence levels > 90% compared
96 to those with active substance abuse (81.7%). Finally, subjects with a history of mental health disorders self-reported a higher pe rcentage of medication adherence below the acceptable rate of 90%. Lastly, subjects part icipating in the SAI were more likely to report adherence levels > 90%. Table 22 Bivariate Analysis Self-Reported Adheren ce: Sociodemographic and HIV Disease Specific Factors Covariates Self-Reported Adherence Pearson Chi-Square (significance level .10 or less) > 90% n (%) < 90% n (%) Sociodemographic Covariates Age (years) 19-29 30-39 40-49 50-59 > 60 28 83 149 86 18 (7.7) (22.8) (40.9) (23.6) (4.9) 3 11 29 12 5 (5.0) (18.3) (48.3) (20) (8.3) Gender Male Female 259 105 (71.2) (28.8) 40 20 (66.7) (33.3) Race White Black Other 199 159 6 (54.7) (43.7) (1.6) 30 27 3 (50.0) (45.0) (5.0) Ethnicity Non-Hispanic Hispanic 268 96 (73.6) (26.4) 41 19 (68.3) (31.7) Income (% Federal Poverty Level) <100 % 101-200 % > 200 % 244 90 30 (67.0) (24.7) (8.2) 48 41 1 (80.0) (18.3) (1.7) Housing Status Permanent Nonpermanent 359 5 (98.6) (1.4) 57 3 (95.0) (5.0) Health Insurance Yes No 239 125 (65.7) (34.3) 49 11 (81.7) (18.3) 6.059, df 1, p=.014
97 Table 22 continued Bivariate Analysis Self-Reported Adheren ce: Sociodemographic and HIV Disease Specific Factors Covariates Self-Reported Adherence Pearson Chi-Square (significance level .10 or less) > 90% n (%) < 90% n (%) HIV Disease Specific Covariates HIV Risk Factor MSM Heterosexual IDU Other 132 197 26 9 (36.3) (54.1) (7.1) (2.5) 14 36 10 0 (23.3) (60.0) (16.7) (0) 9.811, df 3, p=.020 Disease Stage HIV (non-AIDS) AIDS 138 226 (37.9) (62.1) 17 43 (28.3) (71.7) Years Living with HIV < 5 6-10 11-15 >15 149 109 69 37 (40.9) (29.9) (19.0) (10.2) 28 19 8 5 (46.7) (31.7) (13.3) (8.3) Active Substance Abuse Yes No 32 332 (8.8) (91.2) 11 49 (18.3) (81.7) 5.147, df 1, p=.023 Mental Health Disorder Yes No 96 268 (26.4) (73.6) 23 37 (38.3) (61.7) 3.649, df 1, p=.056
98 Table 23 Bivariate AnalysisSelfReported Adherence: ARV and Adherence Counseling and Intervention Specific Factors Covariates Self-Reported AdherencePearson Chi-Square (significance level .10 or less) > 90% n (%) < 90% n (%) ARV Specific Covariates Dosing Frequency Once Daily Twice Daily 113 251 (31.0) (69.0) 19 41 (31.7) (68.3) Type of ARV Regimen Protease Inhibitor NNRTI Triple NRTI 199 145 20 (54.7) (39.8) (5.5) 36 20 4 (60.0) (33.3) (6.7) Daily ARV Pill Burden 2-4 5-8 9-15 151 174 39 (41.5) (47.8) (10.7) 22 30 8 (36.7) (50.0) (13.3) Adherence Counseling and Intervention Received Adherence Counseling Yes No 24 340 (6.6) (93.4) 7 53 (11.7) (88.3) No. of Counseling Sessions 0 1 2 3 4 >5 340 18 2 2 1 1 (93.4) (4.9) (0.5) (0.5) (0.3) (0.3) 53 5 2 0 0 0 (88.3) (8.3) (3.3) (0) (0) (0) Adherence Counseling (minutes) 0 30-60 61-120 >120 340 7 13 4 (96.4) (1.9) (3.6) (1.1) 53 1 3 3 (88.3) (1.7) (5.0) (5.0) Adherence Aids Prescribed Yes No 13 351 (3.6) (96.4) 3 57 (5.0) (95.0) Treatment Condition SAI Usual Care 191 173 (52.5) (47.45) 13 47 (21.7) (78.3) 19.581, df 1, p<.0005
99 The initial factors included in the logi stic regression included income, housing status, health insurance, HIV risk factor, ac tive substance abuse, mental health disorder and treatment condition. Through an iterati ve process factors that did not make significant contributions in the regression were removed and regression was repeated with the remaining factors. Factors were rein serted if the model was negatively affected by the removal of a covariate. Covariates selectively removed from the regression included income, health insurance, HIV risk f actor, and mental health disorder. Housing status was removed because a small sample size related to nonpermanently housed subjects created unstable results. After adjusting for covariates, subjects in the SAI group remained significantly more likely to self-report medication adherence > 90% as compared to the usual care group (adjusted OR = 3.944; 95% CI 2.058, 7.557; p < 0.0005). Additionally, patients with a history of substance abuse were le ss likely to report favorable medication adherence (OR 2.237; CI 1.033, 4.864; p=.041). A pproximately 68% of all cases were explained by this regression model (c-statist ic 0.677). Based on th e logistic regression results, the null hypothesis of no difference in self-reported adherence between the two groups was rejected. Patients who participat ed in the SAI program were almost four times more likely to report adherence levels > 90%. These results are presented in Table 24.
100 Table 24 Logistic Regression Analysis: Summary of Predictors of Self-Reported Adherence (> 90%) Variable Odds Ratio 95% Confidence Interval Wald ChiSquare P Treatment Condition 1.372 3.944 2.058, 7.557 17.017 <.0005 History of Substance Use -0.805 2.237 1.033, 4.864 4.167 0.041 Note Overall model, Chi-Square = 24.566, df =2, p <.0005 Pharmacy Refill Adherence Bivariate analyses were calculated on pharmacy refill adherence and each of the covariates and the treatment condition. The da ta are displayed in Table 25. There were several significant findings. In most of th e age groups, similar perc entages of subjects had both favorable (> 90%) and unfavorable (<90%) pharmacy refill adherence. However, nearly twice as many subjects in the age range of 30-39 years had unfavorable pharmacy refill adherence (29.4%) while only 15.5% of subjects had favorable adherence. An inverse relationship was seen in the age group of 50-59 years: nearly double the percentage of subjects demonstr ated favorable adherence (29.1%) while 16.7% had unfavorable levels. These differe nces were significant (Chi-Square 17.287, df 4, p=.002). White patients also had a higher percentage of favorable pharmacy refill adherence. A higher percentage of subjects with household income <100% of Federal Poverty Level demonstrated lower adheren ce levels (76% compared with 62.3%) while those with income levels between 101-200% had nearly twice the rate of favorable pharmacy refill adherence at a level > 90% (29.5% compared to 17.6%). A greater percentage of subjects without health in surance (38.2%) demonstrated pharmacy refill
101 adherence > 90% compared with insured pa tients (25.5%) (Chi-Square 7.862, df 1, p=.005). Of subjects reporting active substan ce use, almost twice as many demonstrated pharmacy refill adherence <90% (13.2%) compared to those with favorable refill adherence (7.3%). Table 25 Bivariate AnalysisPharma cy Refill Adherence: Soci odemographic and HIV Disease Specific Factors Covariates Pharmacy Refill AdherencePearson Chi-Square (significance level .10 or less) > 90% n=204 n (%) < 90% n=220 n (%) Sociodemographic Covariates Age (years) 18-29 30-39 40-49 50-59 > 60 14 34 95 64 13 (6.4) (15.5) (43.2) (29.1) (5.9) 17 60 83 34 10 (8.3) (29.4) (40.7) (16.7) (4.9) 17.287, df 4, p=.002 Gender Male Female 160 60 (72.7) (27.3) 139 65 (68.1) (31.9) Race White Black Other 137 77 6 (62.3) (35.0) (2.7) 92 109 3 (45.1) (53.4) (1.5) 14.765, df 2, p=.001 Ethnicity Non-Hispanic Hispanic 155 65 (70.5) (29.5) 154 50 (75.5) (24.5) Income (% Federal Poverty Level) <100 % 101-200 % > 200 % 137 65 18 (62.3) (29.5) (8.2) 155 36 13 (76.0) (17.6) (6.4) 9.653, df 2, p=.008 Housing Status Permanent Nonpermanent 215 5 (97.7) (2.3) 201 3 (98.5) (1.5) Health Insurance Yes No 136 84 (61.8) (38.2) 152 52 (74.5) (25.5) 7.862, df 1, p=.005
102 Table 25 continued Bivariate AnalysisPharma cy Refill Adherence: Soci odemographic and HIV Disease Specific Factors Covariates Pharmacy Refill AdherencePearson Chi-Square (significance level .10 or less) > 90% n=204 n (%) < 90% n=220 n (%) HIV Disease Specific Factors covariates HIV Risk Factor MSM Heterosexual IDU Other 88 111 18 3 (40.0) (50.5) (8.2) (1.4) 58 122 18 6 (28.4) (59.8) (8.8) (2.9) 7.09, df 3, p=.069 Disease Stage HIV (non-AIDS) AIDS 90 130 (40.9) (50.1) 65 139 (31.9) (68.1) 3.735, df 1, p=.053 Years Living with HIV < 5 6-10 11-15 >15 96 73 32 19 (43.6) (33.2) (14.5) (8.6) 81 55 45 23 (39.7) (27.0) (21.1) (11.3) Active Substance Abuse Yes No 16 204 (7.3) (92.7) 27 177 (13.2) (86.8) 4.129, df 1, p=.042 Mental Health Disorder Yes No 63 157 (52.9) (51.5) 56 148 (47.1) (48.5) There were no significant differences between pharmacy refill adherence and ARV characteristics and adherence counseling and interventi on specific factors. Lastly, a higher percentage (54.5%) of subjects par ticipating in the SAI had pharmacy refill adherence > 90% compared to those receiving usual care (45.5%) (Chi-square 0.578, df 1, p=.008). These data are displayed in Table 26. The initial factors included in the logistic regression for pharmacy refill adherence included age, race, income, health insurance, HIV risk factor, disease stage, active substance abuse, time associ ated with adherence counse ling and treatment condition. Factors that did not have significant contributions in the regression were removed including time associated with adherence couns eling, and active substance abuse. Health
103 insurance was removed as it was believed that this factor and the treatment condition of SAI explained the same information. Table 26 Bivariate Analysis Pharmacy Refill A dherence: ARV and Adherence Counseling and Intervention Specific Factors Covariates Pharmacy Refill AdherencePearson Chi-Square (significance level .10 or less) > 90% n (%) < 90% n (%) ARV Covariates Dosing Frequency Once Daily Twice Daily 65 155 (29.5) (70.5) 67 137 (32.8) (67.2) Type of ARV Regimen Protease Inhibitor NNRTI Triple NRTI 115 92 13 (52.3) (41.8) (5.9) 120 73 11 (58.8) (35.8) (5.4) Daily ARV Pill Burden 2-4 5-8 9-15 95 104 21 (43.2) (47.3) (9.5) 78 100 26 (38.2) (49.0) (12.7) Adherence Services and Intervention Received Adherence Counseling Yes No 12 208 (5.5) (94.5) 19 185 (9.3) (90.7) No. of Counseling Sessions 0 1 2 3 4 >5 208 10 2 0 0 0 (94.5) (4.5) (0.9) (0) (0) (0) 185 13 2 2 1 1 (90.7) (6.4) (1.0) (1.0) (0.5) (0.5) Adherence Counseling (minutes) 0 30-60 61-120 >120 208 2 9 1 (94.5) (0.9) (4.1) (0.5) 185 6 7 6 (90.7) (2.9) (3.4) (2.9) Adherence Aids Prescribed Yes No 6 214 (2.7) (97.3) 10 194 (4.9) (95.1) Treatment Condition SAI Usual Care 120 100 (54.5) (45.5) 84 120 (41.2) (58.8) 7.578, df 1, p=.006
104 After adjusting for covariates, subjects in the SAI group remained significantly more likely to achieve 90% or more pharmacy refill adherence compared to the usual care group (OR 1.833, CI 1.206, 2.788; p=.005). In th is regression, age was treated as a continuous variable. For every increase in year of age, there was approximately a 5% increase in the likelihood of having a favorable pharmacy refill adherence outcome. White race had a negative association w ith pharmacy refill adherence (OR 0.496; CI .039, 0.749; p=.001). Approximately 68% of all cases were correctly predicted by this regression model (c-statistic 0.683). Based on the logistic regression results, the null hypothesis for hypothesis number two was rejecte d. These results are presented in Table 27. Table 27 Logistic Regression Analysis: Summary of Predictors of Pharmacy Refill Adherence Variable Odds Ratio 95% Confidence Interval Wald ChiSquare P Treatment Condition 0.606 1.833 1.206 2.788 8.038 0.005 Age 0.049 1.050 1.026 1.074 17.633 <.0005 Race -0.701 0.496 0.329 0.749 11.150 0.001 Note Overall model, Chi-Square = 43.012, df =5, p < .0005.
105 Study Aim One: Summary The two hypotheses associated with the first study aim were supported. Logistic regression analyses support significant differe nces in self-reported adherence and pharmacy refill adherence associated with participation in the SAI program. Study Aim Two: Treatment Response Outcomes The purpose of the second study aim was to determine whether patients participating in the SAI program experience improved response to treatment compared to patients receiving usual care, controlling for HIV diseasespecific factors, ARV-specific factors, and sociodemographic factors. Two hypotheses were tested: 1) Patients participating in the SAI program will have better immunologic (CD4 lymphocyte) responses to HAART compared to patients receiving usual care, controlling for selected covariates. 2) Patients participating in the SAI program will have better virologic (HIV RNA) responses compared to patients receiving usual care, controlling for selected covariates. CD4 Lymphocyte Response There were few statistically significant findings in the bivariate analyses of CD4 lymphocyte response and each of the covariates and the treatment condition. Overall, approximately 64% of subjects in this study demonstrated an unfavorable declining CD4 lymphocyte response. Although the difference is not significant, a larger percentage of subjects identifying as Hispanic had a declining CD4 response (57.4% compared with 33.7% for non-Hispanic) (Chi-square 2.912, df 1, p=.088). All 8 of the subjects with nonpermanent housing demonstrated a stable or increasing CD4 response (Chi square 3.147, df 1, p=.076, Yates Correction for Continuity). This information is displayed in Table 28.
106 Table 28 Bivariate Analysis CD4 Lymphocyte Re sponse: Sociodemographic and HIV Disease Specific Factors Covariates CD4 Lymphocyte Pearson Chi-Square (significance level .10 or less) Declining N (%) Stable/Increasing n (%) Sociodemographic Covariates Age (years) 19-29 30-39 40-49 50-59 > 60 13 35 58 36 11 (8.5) (22.9) (37.9) (23.5) (7.2) 18 59 120 62 12 (6.6) (21.8 (44.3) (22.9) (4.4) Gender Male Female 112 41 (73.2) (26.8) 187 84 (69.0) (31.0) Race White Black Other 78 71 4 (51.0) (46.4) (2.6) 151 115 5 (55.7) (42.4) (1.8) Ethnicity Non-Hispanic Hispanic 104 49 (68.0) (32.0) 205 66 (75.6) (24.4) 2.912, df 1 p=.088 Income (% Federal Poverty Level) <100 % 101-200 % > 200 % 113 30 10 (73.9) (19.6) (6.5) 179 71 21 (66.1) (26.2) (7.7) Housing Status Nonpermanent Permanent 0 153 b (0) (100) 8 263 g (3.0) (97.0) 3.147, df 1, p=.076 (Yates Correction for Continuity) Health Insurance Yes No 46 107 (30.1) (69.9) 90 180 (33.2) (66.8) HIV Disease Specific Factors HIV Risk Factor MSM Heterosexual IDU Other 52 82 16 3 (34.0) (53.6) (10.5) (2.0) 94 151 20 6 (34.7) (55.7) (7.4) (2.2) Disease Stage HIV (non-AIDS) AIDS 59 94 (38.6) (61.4) 96 175 (35.4) (64.6) Years Living with HIV < 5 6-10 11-15 >15 61 43 33 16 (39.9) (28.1) (21.6) (10.5) 116 85 44 26 (42.8) (31.4) (16.2) (9.6) Active Substance Abuse Yes No 19 134 (12.4) (87.6) 24 247 (8.9) (91.9) Mental Health Disorder Yes No 46 107 (30.1) (69.9) 73 198 (26.9) (73.1)
107 Of the 271 subjects with a favorable CD4 response, 34.3% received a once daily regimen while 65.7% received a tw ice daily regimen (Chi-square 3.554, df 1, p=.059). Immunologic response based on pill burden was si milar when subjects received 2-4 or 58 pills per day. However, when daily pill burden exceeded 8 pills per day, there were a higher percentage of subjects that had CD4 decline (Chi-square 8.719, df 2, p=.013). Lastly, 68% of subjects in the SAI (n=139) had a favorable immunologic response compared with only 60% of those in usual care (n=132) (Chi-square 3.039 df 1, p=.0816). Table 29 depicts this information. Table 29 Bivariate AnalysisCD4 Lymphocyte Re sponse: ARV and Adherence Counseling and Intervention Specific Factors Covariates CD4 Lymphocyte Pearson Chi-Square (significance level .10 or less) Declining n (%) Stable/Increasing n (%) ARV Specific Covariates Dosing Frequency Once Daily Twice Daily 39 114 (25.5) (74.5) 93 178 (34.3) (65.7) 3.554, df 1, p=.059 Type of ARV Regimen Protease Inhibitor NNRTI Triple NRTI 89 56 8 (58.2) (36.6) (5.2) 146 109 16 (53.0) (40.2) (5.9) Daily ARV Pill Burden 2-4 5-8 9-15 56 71 26 (36.6) (46.4) (17.0) 117 133 21 (43.2) (49.1) (7.7) 8.719, df 2, p=.013
108 Table 29 continued Bivariate AnalysisCD4 Lymphocyte Re sponse: ARV and Adherence Counseling and Intervention Specific Factors Covariates CD4 Lymphocyte Pearson Chi-Square (significance level .10 or less) Declining n (%) Stable/Increasing n (%) Adherence Services and Intervention Received Adherence Counseling Yes No 10 143 (6.5) (93.5) 21 250 (7.7) (92.3) No. of Counseling Sessions 0 1 2 3 4 >5 143 10 0 0 0 0 (93.5) (6.5) (0) (0) (0) (0) 250 13 4 2 1 1 (92.3) (4.8) (150) (0.7) (0.6) (0.6) Adherence Counseling (minutes) 0 30-60 61-120 >120 143 5 4 1 (93.5) (3.3) (2.6) (0.7) 250 3 12 6 (92.3) (1.1) (4.4) (2.2) Adherence Aids Prescribed Yes No 3 150 (2.0) (98.0) 13 258 (4.8) (95.2) Treatment Condition SAI Usual Care 65 88 (42.5) (57.5) 139 132 (51.3) (48.7) 3.039 df 1, p=.0816 The initial factors included in the re gression for CD4 lymphocyte response included ethnicity, housing status, ARV dosi ng, ARV daily pill burden, and treatment condition. Factors that did not have significant contributi ons in the regression were removed (housing status a nd ARV dosing) and regressi on was repeated with the remaining factors. The only significant finding in this regression was related to daily pill burden. Subjects receiving 2-4 tablets per day were less likely to achieve a favorable CD4 lymphocyte response (OR 0.917; CI 0.844, 0.996; p=.039). Approximately 59% of all cases were correctly predicted by this regr ession model (c-statistic 0.594). Based on
109 logistic regression analysis, the null hypothe sis for the third research question was supported as no significant relationship was id entified between the SAI program and CD4 lymphocyte response. These results are presented in Table 30. Table 30 Logistic Regression Analysis: Summary of Predictors of CD4 Lymphocyte Response Variable Odds Ratio 95% Confidence Interval Wald ChiSquare P Ethnicity 0.392 1.480 0.949 2.306 2.996 0.083 Daily Pill Burden -.087 0.917 0.844 0.996 4.287 0.039 Treatment Condition 0.338 1.402 0.935 2.102 2.670 0.102 Note Overall model, Chi-Square = 10.535, df=3, p = .015. HIV RNA Response There were a number of stat istically significant findings in the bivariate analyses of HIV RNA response, the covariates and the treatment condition. The sociodemographic and HIV di sease specific data are displayed in Table 31.
110 Table 31 Bivariate AnalysisHIV RNA Response: Sociodemographic and HIV Disease Specific Factors Covariates HIV RNA Response Pearson Chi-Square (significance level .10 or less) Increasing n (%) Stable/Decreasing n (%) Sociodemographic Covariates Age (years) 19-29 30-39 40-49 50-59 > 60 9 32 74 34 12 (5.6) (19.9) (46.0) (21.1) (7.5) 22 62 104 64 11 (8.4) (23.6 (39.5) (24.3) (4.2) Gender Male Female 113 48 (70.2) (29.8) 186 77 (70.7) (29.3) Race White Black Other 74 83 4 (46.0) (51.6) (2.5) 155 103 5 (58.9) (39.2) (1.9) 6.766, df 2, p=.034 Ethnicity Non-Hispanic Hispanic 122 39 (75.8) (24.2) 187 76 (71.1) (28.9) Income (% Federal Poverty Level) <100 % 101-200 % > 200 % 117 28 6 (72.7) (23.6) (3.7) 175 63 25 (66.5) (24.0) (9.5) 5.112, df 2, p=.078 Housing Status Permanent Nonpermanent 157 4 (97.5) (2.5) 259 4 (98.5) (1.5) Health Insurance Yes No 135 26 (83.9) (16.1) 153 110 (58.2) (41.8) 30.218, df 1 p<.0005 HIV Disease Specific Covariates HIV Risk Factor MSM Heterosexual IDU Other 53 88 15 5 (32.9) (54.7) (9.3) (3.4) 93 145 21 4 (35.4 (55.1) (8.0) (1.5) Disease Stage HIV (non-AIDS) AIDS 50 111 (31.1) (68.9) 105 158 (39.9) (60.1) 3.386, df 1, p=.066 Years Living with HIV < 5 6-10 11-15 >15 62 52 32 15 (38.5) (32.3) (19.9) (9.3) 115 76 45 27 (43.7) (28.9) (17.1) (10.3 Active Substance Abuse Yes No 19 142 (11.8) (88.2) 24 239 (9.1) (90.9) Mental Health Disorder Yes No 47 114 (29.2) (70.8) 72 191 (27.4) (72.6
111 Bivariate analysis suggested there were racial differences related to virologic response. A higher percentage of white pati ents achieved or sustained a virologic response (58.9%) compared to blacks (39.2%) (Chi-square 6.766, df 2, p=.034). A higher percentage of subjects in the lowe st income group experienced increasing HIV RNA while a greater percentage of patients at the highest income level of >200% FPL had a favorable virologic response (Chi-square 5.112, df 2, p=.078). Sixty-two percent of all subjects had a fa vorable virologic response (n=263). Of these, 58.2% had insurance, 41.8% did not. Of the 161 subjects that had unfavorable virologic responses, 16.1% did not have insu rance while 83.9% did have insurance (Chi square 30.218, df 1 p<.0005). Almost two-thirds of the patients had a di agnosis of AIDS. Of patients with an unfavorable HIV RNA response, 68.9% had an AIDS diagnosis. Of the 163 subjects that had a favorable HIV RNA response, 60.1% had an AIDS diagnosis. Although few patients received adherence aids (n=16), 87.5% of them were prescribed to subjects that demonstrated stable or declining HIV RNA (Chi-square 4.58, df 1, p=.032). Lastly, 61.6% of subjects part icipating in the SAI demonstrated favorable virologic responses compared to 38.4% of those in usual care (Chi-square 50.442, df 1, p<.0005). This data is presented in Table 32.
112 Table 32 Bivariate AnalysisHIV RNA Res ponse: ARV and Adherence Counseling and Intervention Specific Factors Covariates CD4 Lymphocyte Pearson Chi-Square (significance level .10 or less) Declining n (%) Stable/Increasing n (%)) ARV Specific Covariates Dosing Frequency Once Daily Twice Daily 49 112 (30.4) (69.6) 83 180 (31.6) (68.4) Type of ARV Regimen Protease Inhibitor NNRTI Triple NRTI 89 65 7 (55.3) (40.4) (9.1) 146 100 17 (55.5) (38.0) (6.5) Daily ARV Pill Burden 2-4 5-8 9-15 61 76 24 (37.9) (47.2) (14.9) 112 128 23 (42.6) (48.7) (8.7) Adherence Services and Intervention Received Adherence Counseling Yes No 8 153 (5.0) (95.0) 23 240 (8.7) (91.3) No. of Counseling Sessions 0 1 2 3 4 >5 153 8 0 0 0 0 (95.0) (5.0) (0) (0) (0) (0) 240 15 4 2 1 1 (91.3) (5.7) (1.5) (0.8) (0.4) (0.4) Adherence Counseling (minutes) 0 30-60 61-120 >120 153 4 4 0 (95.0) (2.5) (2.5) (0) 240 4 12 7 (91.3) (1.5) (4.6) (2.7) Adherence Aids Prescribed Yes No 2 159 (1.2) (98.8) 14 249 (5.3) (94.7) 4.58, df 1, p=.032 Treatment Condition SAI Usual Care 42 119 (26.1) (73.9) 162 101 (61.6) (38.4) 50.442, df 1, p<.0005 The initial factors included in the regression for HIV RNA response included race, income, health insurance, HIV diseas e stage, adherence aids prescribed and treatment condition. Factors that did not have significant contributions in the regression
113 were removed (race, health insurance and in come) and regression was repeated with the remaining factors. After adjusting for covari ates, subjects who participated in the SAI program were over four and a half times more likely to achieve a favorable virologic response (OR 4.573; CI 2.953, 7.080; p<.0005). Subj ects who received an adherence aid were almost seven times more likely to ach ieve a favorable VL response (OR 6.87; CI 1.473, 32.072; p=.014). Approximately 73% of all cases were correctly predicted by this regression model (c-statistic 0.725 ). Based on this logistic regression, the null hypothesis of no differences in virologic response between the SAI and usual care group for hypothesis number four was rejected. Th ese results are presented in Table 33. Table 33 Logistic Regression Analysis: Summary of Predictors of HIV RNA Response Variable Odds Ratio 95% Confidence Interval Wald ChiSquare p Treatment Condition 1.520 4.573 2.953 7.080 46.411 <.0005 Race 0.465 1.592 1.039 2.438 4.564 0.033 Adherence Aids 1.928 6.873 1.473 32.072 6.015 0.014 Note Overall model, Chi-Square = 63.999, df =3, p <.0005 Study Aim Two: Summary The first hypothesis associat ed with study aim two was re jected as there were no significant differences in CD4 lymphocyte response between the SAI and usual care group. The second hypotheses was supporte d as logistic regression analyses
114 demonstrated significant diffe rences in HIV RNA response associated with the SAI program. Summary This chapter has presented the statis tical analyses for the investigation. Demographic results were presented first, fo llowed by the results of bivariate analyses between the adherence outcomes, treatment out comes, treatment groups and covariates. Finally, the results of the l ogistic regression for each study aim were presented. Three hypotheses were supported; one was rejected. In the concluding chapte r, these results will be discussed along with a discus sion of the implications for future research and nursing practice.
115 CHAPTER 5: DISCUSSSION, CONC LUSIONS AND RECOMMENDATIONS Introduction This final chapter presents a synthesis of the research results with a discussion of the findings, conclusions, study limitations a nd implications for clinical practice. Recommendations for dissemination of the findi ngs and for future research are proposed. Summary of the Study The purpose of this retrospective comp arative study was to better understand the effects of an existing anti retroviral access prog ram on adherence to HAART and response to treatment compared to patients enrolled in usual care. In the structured adherence intervention (SAI) staff closely monitored monthly HIV medication refills and provided structured adherence interventions when indi cated. Patients receiving usual care were enrolled in a Medicaid-funded medication access program and did not receive ongoing medication refill monitoring and structured a dherence intervention. Both patient groups received their ARV medications and outpatien t HIV medical care from a single treatment center and pharmacy. The study included 424 subjects comparably distributed between the usual care and SAI group. Bivariate analyses were used to identify significant associations between the usual care and SAI group re garding sociodemographic ch aracteristics, HIV disease related factors, ARV-related characteristics and utilization of a dherence services and intervention. Logistic regre ssion was performed to identify predictors of self-reported medication adherence, pharmacy refill adherence, CD4 lymphocyte response and HIV
116 RNA response. This research provided valu able information related to antiretroviral adherence and treatment outcomes for patients participating in usual care and a statebased antiretroviral access program. The study is unique in that no known investigations have previously tested a structured progr ammatic intervention on ARV adherence and HIV treatment outcomes. Discussion and Conclusions The following is a discussion of the fi ndings according to the study aims and research questions in the study along with the conc lusions that may be drawn from this research study. Study Aim One The first study aim was to determine whet her patients participating in the SAI program experienced higher levels of adhere nce compared to patients receiving usual care, controlling for adherence services and intervention, HIV diseas e-specific factors, ARV-specific factors, and sociodemographic factors. To answer the first research question, Is there a difference in self-reported adherence in s ubjects participating in the SAI program compared to those who receive usual care?, logist ic regression was performed to test the null hypot heses that there were no diffe rences between self-reported adherence between participan ts in the usual care group of subjects and the subjects participating in the SAI. After controlling for covariates, subjects in the SAI group were significantly more likely to self -report medication adherence > 90% as compared to the usual care group (OR = 3.944; 95% CI 2.058, 7.557; p < 0.0005) and the null hypothesis was rejected.
117 To answer the second research question, I s there a difference in pharmacy refill adherence in subjects partic ipating in the SAI program co mpared to those who receive usual care?, logistic regression was performed to test the null hypothesis that there were no differences between pharmacy refill adherence between participants in the usual care group of subjects and the subjects participating in the SAI. After adjusting for covariates, subjects in the SAI group remained significan tly more likely to achieve 90% or more pharmacy refill adherence compared to the usual care group (OR 1.833, 95% CI 1.206, 2.788; p=.005). The null hypot hesis was rejected. Several unexpected findings were seen in this study including low overall utilization of the adherence specialist and comparable use of the adherence specialist between the two study groups. The majority of the study populat ion (92.7%) did not receive any adherence counseling or intervention (n=393) from the adherence specialist. A total of only 31 subjects (7.3%) received at least one face-to-face counseling session with the adherence specialist. The range of counseling time was 30-360 minutes with a mean of 91 minutes during the 6 month study period. Fewer than 4% of all patients received adherence aids such as pill boxes, customized medication schedules, and alarm watches (n=16) from the adherence specialist. There are several potential explanations for the unexpected low utilization of the adherence specialists services. Primar y healthcare providers (PCPs) delivering outpatient care services to the subjects ma y have provided adherence interventions on their own without initiating form al consultation with the adherence specialist. Similarly, PCPs may have initiated the use of pill box es and may have developed detailed written medication schedules without the involvement and knowledge of the adherence
118 specialist. If these interventions were provi ded by the PCP, they would not have been captured by the databases used in this study. Patients may also have received adherence education and counseling services from comm unity case management organizations that receive Ryan White Grant funding specifically for these purposes. Similarly, these interventions could not have been m easured and included in this study. Based on the embedded procedural and admi nistrative processes associated with the ADAP, it seemed likely that clients in this program would demonstrate greater utilization of adherence services and intervention then the us ual care group. In this study, utilization of adherence servic es between the two groups was comparable. It is possible that PCPs and other staff were accustomed to providing adherence support to patients in the SAI group and consequently extended th ese interventions to all patients as a component of routine care. Healthcare providers may have had little knowledge of the patients method of medication access and conseq uently delivered comparable services to all patients within the normal course of health care delivery. Surprisingly, there were no significant differences in pharmacy refill adherence, self-reported adherence, and HIV RNA res ponse related to ARV pill burden. While the literature supports improved adherence with lower pill burden, this study showed comparable adherence and treatment outcomes regardless of ARV pill burden. The largest percentage of subjects (48.1%) had a pill burden of 5-8 ARV pills per day while only 11.1% had daily pill burden of 9-15 a nd 43.2% received 2-4 per day. Although immunologic response based on pill burden was si milar whether subjects received 2-4 or 5-8 pills per day, a higher perc entage of subjects experien ced CD4 decline when daily pill burden exceeded 8 pills (Chi-square 8.719, df 2, p=.013).
119 Pill burden for treatment of other health disorders such as diabetes, psychiatric conditions, cardiovascular and metabolic disord ers was not assessed in this study. It is possible that HAART bill burden was minimal compared to pill burden associated with the treatment of other health conditions. Measurement of overall pill burden may better explain any potential diffe rences in adherence and treatment outcomes. Study Aim Two The second study aim was to determine whet her patients participating in the SAI program experienced improved response to tr eatment compared to patients receiving usual care, controlling for HIV disease-specif ic factors, ARV-specifi c factors, and sociodemographic factors. To answer the first research question, Is there a difference in CD4 lymphocyte response in subjects particip ating in the SAI program compared to those who receive usual care? logistic regression was performed to test the null hypotheses that there were no differences between CD4 lymphocyte response in the usual care group of subjects and the subjects participating in the SAI. After controlling for covariates, there were no significant differences between the two gr oups and the null hypothesis was supported (OR 1.402; CI: 1.402,2.102; p = 0.102). This unexpected finding may be explaine d by several factors. Most notably, expected CD4 lymphocyte response occurs mo re slowly compared to HIV RNA response which occurs more rapidly when initiati ng ARV therapy (Bartlett & Gallant, 2005; Nieuwkerk & Oort, 2005). It is likely that a six-month observation period may have been inadequate to fully appreciate the immunol ogic response to therapy. CD4 lymphocytes are also affected by diurnal and seasonal variat ions. Some clinicians prefer to monitor
120 the percentage of CD4 lymphocytes rather th an the absolute numb er (Bartlett & Gallant, 2005), but not all laboratories provide this additional measurement. Unfortunately CD4 percentages were not available in this study. The subjects in this study also included a broad mix of clients at all ranges of HIV dis ease. Patients starting initial therapy would be expected to have a ro bust CD4 lymphocyte response while it would be unlikely for those chronically infected and on long-term th erapy to experience a si gnificant response. Lastly, the sample size was lower than exp ected and there may not have been enough power associated with this sample size to detect a small change in the CD4 lymphocyte response. To answer the final research question, Is there a difference in HIV RNA response in subjects participating in the SAI program compared to those who receive usual care? logistic regressi on was performed to test the null hypotheses that there were no differences in the HIV RNA response between the two groups. After controlling for covariates, subjects who participated in the SAI program were over four and a half times more likely to achieve a favorable viro logic response (OR 4.573; 95% CI 2.953, 7.080; p<.0005). There was a statistically significan t difference between the two groups and the null hypothesis of no difference betw een the groups was rejected. Limitations of the Study There are several limitations to consider in this study. Each of these will be reviewed in the following section. The sample was biased because all subj ects were already enrolled in an AIDS drug assistance program and they all receiv ed care from one outpatient clinic. Additionally, all subjects received medicat ion from one pharmacy. Clinical, pharmacy
121 and medication access programs were all at the sa me site. As a result, the findings may not be representative of the true population in the state-wide ADAP. The sample did represent racial and ethnic dive rsity consistent with HIV-infected patients within the local community. Another source of bias is related to self-reported meas urement of adherence. Although self-report is one of the most common methods of assessing medication adherence, inaccuracy may result due to im precise or inconsistent questioning, patient forgetfulness and poor recall, or the patient s desire to provide socially desirable responses along with a desire to please the healthcare provider and prevent criticism. Consequently, when self-reporting methods are used to assess adherence, levels are frequently over-estimated. The retrospective research design was purposely selected to minimize several possible confounders that existed in the y ears 2006 and 2007. Medicare D prescription drug plans were initiated in January 2006. Clients expe rienced unique barriers to medication access, unexpected loss of previous healthcare benefits and interruptions in their supply of medication. Wh ile most of the Medicare D complications resolved by 2007, eligibility requirements for Florida ADAP and other local f unding plans occurred in 2007, once again disrupting th e normal operations of ADAP. A priori power estimates suggested a mi nimum sample size of 678 subjects were required for power of 0.80. A large number of potential subjects were unexpectedly excluded from analysis because they did not use the on-site pharmacy. Consequently, this study is inadequately powered to detect the effect size specified in the research design.
122 The findings of this research may not be generalizable to other populations. The sample of the participants may not reflect the overall population of those with HIV since the study site was a public clinic frequently us ed by those who are indigent or have public insurance such as Medicare or Medicaid. Patients with commercial insurance tend to seek private practices for their HIV care, so those employed in jobs that provide insurance were underrepresented in this sample, as well as those with high income. These findings cannot be applied to populations that were not represented in the subject groups. Further studies are recommended acr oss various geographic areas, ethnic areas and other clinical settings. Although this study examined a number of covariates, it is possible that there are unknown or additional variables that might impact adherence and treatment outcomes in this population. Examples might include leve l of education, social support, quality of life, number of previous antiretroviral re gimens, presence of ARV resistance, and participation in a clinical drug study. Adherence educati on and counseling provided by case managers in the community may also have an effect on pharmacy refill adherence, self-reported adherence and treatment outcomes. The short time of follow-up may have limited the ability to measure the longrange effect of the SAI. Although the relatively short follo w-up time in this study may be inadequate to fully appreciate the vi rologic and immunologic response to therapy, extending the study period might result in a dditional confounders. For example, the population utilizing this public c linic is often transi ent, incarcerated and often lost to follow-up.
123 This study examined multiple variables that could impact both treatment response and adherence. It is difficult to attribute th e true effect of each va riable. Future study using path analysis might elucidate the true effect of each covariate. Three types of antiretroviral therapy we re considered in this study: (1) nonnucleoside reverse transcriptase based; (2) protease inhibitor based; and (3) triple nucleoside reverse transcriptase based. The pr otease inhibitor (PI) rit onavir is frequently administered in a low-dose along with a pr imary PI as a pharmacokinetic booster to the primary PI. Boosting a PI with ritonavi r increases drug exposure and prolongs the plasma half-life of the primary protease i nhibitor. This allows for reduced dosing frequency and pill burden and may improve overall adherence to the regimen (National Institutes of Health, 2006). Ritonavirboosting was not assessed in this study and may be an important characteristic to assess in future studies since boosting can improve adherence through reduced pill burden a nd greater drug exposur e could result in improved virologic and immunologic treatment re sponse outcomes. Nelfinavir is the only protease inhibitor that ca nnot be effectively boosted by r itonavir (Bartlett & Gallant, 2005). Since a number of subjects received ne lfinavir as a component of HAART, it may be helpful to study both boosted and unboosted PI-based regimens. This study included patients who only us ed one consistent pharmacy to obtain their medication. A large number of patients (n=569) us ed alternative pharmacies throughout the community. Although the demogr aphic characteristics of this population are similar to the sample of patients in the usual care group, the pharmacy refill adherence rates are not known. Future st udies should consider investigating the pharmacy refill rates at community pharmacies to investigate whether there are any
124 unique features from the on-site pharmacy co mpared to community pharmacies. Since the dedicated on-site pharmacy is used to providing adherence messages within general conversation with patients, it is theoretically possi ble that even this communication may have an effect on patient adherence to medica tion therapy. It is not known what type of adherence messages or encouragement is provided by community pharmacies. There was little effect on adherence relate d to active substance abuse and mental health disorders despite literature which s upports a negative impact on adherence. The lack of effect in this study may be related to the coding of the substance abuse and mental health diagnoses or the small sample size. Th e covariates related to mental health and substance abuse were based on healthcare provider coding and documentation in the medical record. There were no clearly defined objective or operational definitions related to these diagnoses. It is possible that these diagnoses were under-diagnosed, overdiagnosed or misdiagnosed. Significance This study demonstrates a si gnificant effect on self-repor ted adherence, pharmacy refill adherence, and HIV RNA response associ ated with participation in the AIDS drug assistance program. There are potential unknow n covariates that ma y be involved with adherence and future qualitative inquiry ma y be helpful in identifying them and their potential effect on adherence. This will be discussed in another section. Funding for AIDS Drug Assistance Pr ograms is provided by the federal government and often supplemented with indivi dual state funds. With limited national and state funding for these programs, it is impera tive that funds be used as effectively as possible to serve the greatest number of clie nts possible and to produce the most optimal
125 clinical outcomes. This study demonstrat es significant improvement in medication adherence as well as treatment outcomes asso ciated with participation in one ADAP and can serve as a model to local, regional a nd national programs as a potential means to optimize medication adherence and treatment response with limited resources. In the State of Florida, a cen tralized database contains administrative and clinical data related to each ADAP participant in cluding CD4 lymphocyte counts, HIV RNA results, antiretroviral specific informa tion and sociodemographic information. The findings from this study can serve as a st arting point for program administrators to analyze statewide data to identify treatment response rates and progr am effectiveness. Additionally, administrators coul d utilize this database to id entify problematic areas or areas that may need additional resources base d on observation of clinical outcomes as measured by CD4 lymphocyte and HIV RNA response. Ongoing discussion is occurring on a nationa l level related to the collection and study of clinical outcome data from the va rious AIDS Drug Assistance Programs within the United States. This study demonstrates th e potential benefit from examining these types of data and the potential benefits fo r program administrators, clinicians and patients. Components of the structured adherence program may be appropr iate for settings with limited technology or limited resources. Closely monitoring pharmacy refills and proactively implementing communication with pa tients before they run out of medication may be quite appropriate for rural or even si tes in the developing world in an effort to improve adherence and treatment outcomes.
126 This study contributes to our knowledge of the difficulties in fully understanding the patient-level determinants of ARV me dication adherence. There are numerous variables that affect adheren ce and ongoing research is indica ted to continue to increase our understanding of this complex process. Results of this study provide a foundation for future research exploring issues of medication adherence, pharmacy refill adherence, and participation in structured medication access program. It is important that the findings from this study are communicated to local staff involved in the study as well as th e administrators at the regiona l, state and national level. The findings clearly support better adherence and clinical outcomes in the population participating in the medication access program By disseminating this information to clinicians and administrators, others may be encouraged to implemen t similar procedures for monitoring pharmacy refills and initiating structured treatment intervention. With hundreds of medication access programs across the United States, it is important for clinicians and administrators to recognize the potential impact of their programs on adherence and treatment response. Several immediate plans are in process to disseminate the findings of this study. Locally, the staff associated with the study si te will be informed of the findings. On a community level, attendees at the local Associ ation of Nurses in AIDS Care meeting will be provided with an overview of the study and its findings. At a regi onal level, the study results will be presented to a coalition of government repres entatives, corporations and community advocates representing fourteen so uthern states and th eir respective ADAPs. Lastly, the study will be submitted for publ ication in peer-reviewed journal.
127 Implications for Nursing Practice The findings of this study support the ability of a structured adherence intervention within a medicat ion access program to effec tively influence clinical outcomes and adherence associated with the tr eatment of HIV-infected patients. Nurses and other healthcare providers play a key ro le in providing ongoing education related to ARV medication including pr oper administration, manage ment of medication side effects, adherence to therapy, and adherence to clinical care. Nurses are in a key role to formally and informally assess adherence and to refer patients for specialized adherence education and counseling as needed. Nurses often have more contact with the patient than any other member of the health care t eam and are in a pivotal position to assess adherence and implement creative strategi es to improve adherence and increase knowledge. Nurses should have strong interviewing skil ls to be able to elicit information regarding adherence in a prof essional and nonjudgmental manner. Nurses are in a key role to recognize nonadherence and initiate appropriate adherence interventions as quickly and effectively as possible. Nurse practitioners (NPs) c ontinue to serve as primary care providers for many patients with HIV infection and are instrument al in initiating and ma naging antiretroviral therapy. By providing thorough patient educati on, selection of tolerable agents that the patient is able to adhere to, and prompt referral to adherence specialists, NPs can influence adherence in a positive and proactiv e manner. Nurse researchers are active in adherence research and conti nue to contribute to this growing body of knowledge.
128 There continues to be an ongoing need to develop effective adherence interventions and to increase awareness related to the importance of medication adherence among patients living with HIV diseas e. It is important to find adherence interventions that are cost-eff ective and replicable outside of a research setting. It is equally important to encourage o ngoing educational activ ities for patients, nurses, and other health care providers to in crease their knowledge and awareness related to medication adherence and pharmacy refi ll adherence as a means to improve immunologic and virologi c success with HAART. There is a growing need for effective pa tient education rega rding readiness for treatment, HIV illness management, drug-drug interactions, potential drug side effects and side effect management. The complex ity of treatment and the side effects of treatment make this an important area for nursi ng practice. Similarly, there is a need for further development of a standardized de finition of adherence and valid objective measures of adherence that are appropriate fo r both clinical research and clinical care settings. Future research needs to address the best method to assess adherence to ensure reliability and validity, since this is the cruc ial outcome measure in all adherence research and because adherence has a direct impact on patient morbidity and mortality. The selfreport method of measuring adhere nce may not be the most useful predictor of adherence. Recommendations for Future Study Based upon the review of related studie s and the findings from this study, a number of recommendations are made for future research in this area. This study could be replicated using a prospective design w ith a larger sample size that encompasses different geographic areas and which follows subjects for a longer time period. This
129 would generate findings that would be more representative of the population with HIV and AIDS and would have the power to more accurately measure the effects of covariates associated with adherence and treatment res ponse. It would be helpful to measure HIV RNA response and CD4 count response as a continuous variable over many months to many years. Inclusion of CD4 lymphocyte pe rcentage may be an additional variable to consider in the study. A longitudinal study design might permit l onger follow-up to determine if adherence and treatment outcome responses are retained for long periods of time. Future studies should consider distingui shing between ritonavir boosted protease inhibitor based regimens and non-ritonavi r boosted PI regimens. Although non-boosted PI regimens are becoming less common, there were a significant am ount of nelfinavir based regimens in the study (non-boosted). Non-boosted PI regimens are traditionally less potent and durable that boosted-PIs and ma y have less favorable treatment outcomes. While this study defined three types of antir etroviral therapy regimens (non-nucleoside reverse transcriptase based, protease inhibitor based, a nd triple nucleoside reverse transcriptase based), future studies should c onsider incorporating newer regimens that emerged in 2007 and 2008 including entry inhibi tor based, integrase inhibitor based, and second generation NNRTI-based. A qualitative research component would be very useful in future research. Qualitative inquiry may help to identify per ceptions and behaviors associated with the SAI, other adherence strategies used by patie nts (such as cellular phone alarms, internet based systems, and other personal strategies), factors identified by the clinical population to be important in their adherence to medi cations, and the burden of chronic disease.
130 Qualitative inquiry involving staff of both the medication access program and pharmacy may also generate findings that influen ce adherence such as adherence messages delivered within the normal course of business communication, informal teaching messages, and other verbal and nonverbal messages. Ongoing research in this field should include the study of clients that use community pharmacies as well as those th at use pharmacies that deliver monthly medications directly to patients homes. General community pharmacies (as opposed to community HIV-specialty pharmacies) ma y not be as knowledgeable about HIV treatment agents and may not understand the im portance of high levels of adherence to medication refills. More pharmacies are offering free home delivery of HIV medications as a means to increase their business while providing a valuable se rvice and convenience to patients. The adherence implications of these services have not been formally studied and published. It is also important to consider th e effect of community-based adherence educators on patient adherence and treatme nt outcomes. Although these programs are often funded by Ryan White Grant funding, they frequently operate with case management and social work agencies with little or no contact with medical care providers, AIDS drug assistance programs, and client pharmacies. Qu alitative studies of these programs may provide important info rmation that impact clinical care and pharmacy refill behaviors. As the population affected by HIV continues to impact more people of color and more minorities, it is im portant to consider th e potential impact of cultural barriers and language barriers of subject s whose primary language is nonEnglish.
131 While the findings of this study demonstrate improved adherence and treatment response associate with the ADAP, it would be beneficial to i nvestigate the costs associated with the program and determine if the program is indeed cost-effective for the adherence and outcome benefits associated with the program. W ith dwindling federal and state funding of these programs, this information is critical in ensuring ongoing funding of these valuable programs. Summary The purpose of this retrospective comp arative study was to better understand the effects of an existing anti retroviral access prog ram on adherence to HAART and response to treatment compared to usual care. In the structured adherence intervention (SAI) providers closely monitored monthly HIV me dication refills and provided structured adherence intervention when indicated. Patient s receiving usual care were enrolled in a Medicaid-funded medication access program and did not receive ongoing medication refill monitoring and structured adherence in tervention. Both patient groups received their ARV medications and outpa tient HIV medical care from a single treatment center and pharmacy Three of the four hypotheses were confirme d in this study. Patients participating in the SAI demonstrated higher levels of both self-reported and pharmacy refill adherence compared to patients receiving usual care. Patients in the SAI were almost four times more likely to self-report > 90% adherence (OR 3.94, p<.0005) compared to the usual care group and almost twice as likely to achie ve favorable pharmacy refill adherence (OR 1.83, p=.005). Although patients participating in the SAI program demonstrated better virologic (HIV RNA) responses to HAART co mpared to patients receiving usual care,
132 immunologic (CD4 lymphocyte) responses to HAART were not signi ficantly different compared to subjects in the usual care progr am. Patients in the SAI were more than four times as likely to achieve a favorable HIV RNA response compared to those in the SAI (OR=4.57, p<.0005).
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158 Appendix A
159 Appendix A continued
160 Appendix B
About the Author Donald E. Kurtyka received a Bachelor of Science degree in Nursing from Fitchburg State College in 1980, a Master of Business Administration degree in health care management from Western New England College in 1983, and a Master of Science degree in Family Health Nursing from th e University of South Florida in 1992. As a board certified Family Nurse Practiti oner he maintains a clinical practice in the specialty of HIV/AIDS care. He is an in structor in the Univers ity of South Floridas College of Medicine, Division on Infectious Diseases and Intern ational Medicine and Director of HIV Services for Tampa General Ho spital. Dr. Kurtyka is certified as an HIV Specialist by the American Academy of HIV Medicine.