Patterns and predictors of survival following an HIV/AIDS-related neurologic diagnosis

Transcription

1 University of Iowa Iowa Research Online Theses and Dissertations 2012 Patterns and predictors of survival following an HIV/AIDS-related neurologic diagnosis Martha Lydia Carvour University of Iowa Copyright 2012 Martha L. Carvour This dissertation is available at Iowa Research Online: Recommended Citation Carvour, Martha Lydia. "Patterns and predictors of survival following an HIV/AIDS-related neurologic diagnosis." PhD (Doctor of Philosophy) thesis, University of Iowa, Follow this and additional works at: Part of the Clinical Epidemiology Commons

2 PATTERNS AND PREDICTORS OF SURVIVAL FOLLOWING AN HIV/AIDS-RELATED NEUROLOGIC DIAGNOSIS by Martha Lydia Carvour An Abstract Of a thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Epidemiology in the Graduate College of The University of Iowa May 2012 Thesis Supervisor: Professor James C. Torner

3 1 ABSTRACT Infection with human immunodeficiency virus (HIV) and progression to acquired immune deficiency syndrome (AIDS) often result in neurologic and neuropsychiatric changes, although the prognostic information available for patients affected by HIV/AIDSrelated neurologic diagnoses has been limited. The objective of the present study was to characterize the patterns and predictors of survival, including the impacts of antiretroviral therapy (ART) use and potential factors in healthcare access and disparity, among patients with one or more of the following conditions: cryptococcosis, toxoplasmosis, primary central nervous system lymphoma, progressive multifocal leukoencephalopathy, and HIVassociated dementia. To accomplish this, a cohort was drawn from the Iowa HIV/AIDS reporting system, and a non-independent, university-based cohort was then used to validate the analyses conducted for the statewide sample. Patterns of ART use were identified in each cohort using logistic regression, and survival analyses were conducted using Kaplan- Meier analysis, Cox regression, and accelerated failure time modeling. Survival was poor in both cohorts, although the university-based setting (University of Iowa Hospitals and Clinics) was associated with better overall survival. Of 230 persons in the statewide cohort, 77.0% were deceased by the end of the study period ( ), and the median survival was 1.13 years (95% CI: 0.90 to 1.86 years, n=225). By contrast, 56.4% of the university-based cohort was deceased by the end of the study period ( ), and the median survival in this group was 3.04 years (95% CI: 1.79 to years, n=172). Both cohorts were predominantly male, non-hispanic white, and residents of a small metropolitan area at the time of the AIDS diagnosis. ART use had a strong protective effect on survival in both cohorts. Use of ART among patients diagnosed during the era of highly active antiretroviral therapies (HAART)

4 2 was associated with an 80% reduction in the rate of death (HR=0.20, 95% CI: 0.08 to 0.46) compared to the non-users diagnosed during the pre-haart era (that is, prior to 1996), after adjustment for age, race, birth sex, healthcare facility type, opportunistic infection count, HIV transmission risk category, neurologic condition, years since AIDS diagnosis, and timing of neuro-aids in a Cox regression model. In the UIHC cohort, the adjusted expected survival time among ART/HAART users was (95% CI: to 99.48) times that among non-users. Women had significantly poorer outcomes than men in the statewide cohort (adjusted HR=2.31, 95% CI: 1.22 to 4.35), and a similar, non-significant trend was observed among university-based cases. Secondary analyses suggested that this difference persisted over the course of the epidemic and was not attributable to differential ART response among men and women. Evidence for a role of disease severity, psychosocial support, and/or psychiatric comorbidity in the differential survival of men and women was identified. This study provides useful prognostic data for patients and providers and may guide future research efforts aimed toward improved survival for neuro-aids patients. The survival disadvantage of women compared to men should be confirmed and the mechanisms underlying this disparity elucidated. Meanwhile, clinical and public health efforts might be directed towards screening, treatment, and support for women affected by neuro-aids, including potential assessment of comorbid psychiatric disorders. Abstract Approved: Thesis Supervisor Title and Department Date

5 PATTERNS AND PREDICTORS OF SURVIVAL FOLLOWING AN HIV/AIDS-RELATED NEUROLOGIC DIAGNOSIS by Martha Lydia Carvour A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Epidemiology in the Graduate College of The University of Iowa May 2012 Thesis Supervisor: Professor James C. Torner

6 Graduate College The University of Iowa Iowa City, Iowa CERTIFICATE OF APPROVAL PH.D. THESIS This is to certify that the Ph.D. thesis of Martha Lydia Carvour has been approved by the Examining Committee for the thesis requirement for the Doctor of Philosophy degree in Epidemiology at the May 2012 graduation. Thesis Committee: James C. Torner, Thesis Supervisor Charles F. Lynch Elizabeth A. Chrischilles Tara C. Smith Dawei Liu Jeffery L. Meier

7 ACKNOWLEDGEMENTS I am grateful for the interest, commitment, and patience of the many advisors and collaborators who have had a hand in this project. Dr. James Torner, my thesis advisor, has been a steady force behind this work and an excellent, apparently indefatigable, guide through the challenges inherent in HIV/AIDS epidemiologic research and the process of data acquisition. Likewise, Dr. Charles Lynch has devoted many hours to this research as an advisor and collaborator and has always encouraged me to turn good into better and better into excellent, and always to do so with good humor. Dr. Jeffery Meier has repeatedly advocated for the project, has provided counsel from the invaluable standpoint of an infectious disease clinician, and has helped to make access to both sets of cohort data a reality. Similarly, Drs. Elizabeth Chrischilles, Tara Smith, and Dawei Liu have contributed advice and support to the project since its inception. I have had good fortune, also, in that all six members of my thesis committee have overseen some component of my epidemiologic or clinical coursework, including those courses which have given rise to the methods and concepts chosen for this project. Randy Mayer, Jerry Harms, and Srini Madhavan of the Iowa Department of Public Health (IDPH) Bureau of HIV, STD, and Hepatitis have provided the statewide cohort data and have kindly offered their time, advice, and feedback about the project on many occasions. Lisa Sturtz of the University of Iowa Hospitals and Clinics (UIHC) Joint Office for Compliance, Rina Chaudhary and Molly Holst of the UIHC HIV Program, and Jason Brubaker of the State Health Registry of Iowa were all directly involved in data access for the university-based cohort. I am also appreciative of the efforts of Jill France and Donna Johnson of the IDPH Vital Records Bureau in response to our data use applications. ii

8 The Medical Scientist Training Program and the Clinical and Translational Science Award fellowship program have provided many forms of support throughout the course of my training. Lastly, I am grateful to Dr. Margaret Chorazy for her thoughtful feedback about an earlier draft of this thesis and to all of the family members, friends, colleagues, students, patients, and clinician mentors who have offered new perspectives at every turn. iii

9 ABSTRACT Infection with human immunodeficiency virus (HIV) and progression to acquired immune deficiency syndrome (AIDS) often result in neurologic and neuropsychiatric changes, although the prognostic information available for patients affected by HIV/AIDSrelated neurologic diagnoses has been limited. The objective of the present study was to characterize the patterns and predictors of survival, including the impacts of antiretroviral therapy (ART) use and potential factors in healthcare access and disparity, among patients with one or more of the following conditions: cryptococcosis, toxoplasmosis, primary central nervous system lymphoma, progressive multifocal leukoencephalopathy, and HIVassociated dementia. To accomplish this, a cohort was drawn from the Iowa HIV/AIDS reporting system, and a non-independent, university-based cohort was then used to validate the analyses conducted for the statewide sample. Patterns of ART use were identified in each cohort using logistic regression, and survival analyses were conducted using Kaplan- Meier analysis, Cox regression, and accelerated failure time modeling. Survival was poor in both cohorts, although the university-based setting (University of Iowa Hospitals and Clinics) was associated with better overall survival. Of 230 persons in the statewide cohort, 77.0% were deceased by the end of the study period ( ), and the median survival was 1.13 years (95% CI: 0.90 to 1.86 years, n=225). By contrast, 56.4% of the university-based cohort was deceased by the end of the study period ( ), and the median survival in this group was 3.04 years (95% CI: 1.79 to years, n=172). Both cohorts were predominantly male, non-hispanic white, and residents of a small metropolitan area at the time of the AIDS diagnosis. ART use had a strong protective effect on survival in both cohorts. Use of ART among patients diagnosed during the era of highly active antiretroviral therapies (HAART) iv

10 was associated with an 80% reduction in the rate of death (HR=0.20, 95% CI: 0.08 to 0.46) compared to the non-users diagnosed during the pre-haart era (that is, prior to 1996), after adjustment for age, race, birth sex, healthcare facility type, opportunistic infection count, HIV transmission risk category, neurologic condition, years since AIDS diagnosis, and timing of neuro-aids in a Cox regression model. In the UIHC cohort, the adjusted expected survival time among ART/HAART users was (95% CI: to 99.48) times that among non-users. Women had significantly poorer outcomes than men in the statewide cohort (adjusted HR=2.31, 95% CI: 1.22 to 4.35), and a similar, non-significant trend was observed among university-based cases. Secondary analyses suggested that this difference persisted over the course of the epidemic and was not attributable to differential ART response among men and women. Evidence for a role of disease severity, psychosocial support, and/or psychiatric comorbidity in the differential survival of men and women was identified. This study provides useful prognostic data for patients and providers and may guide future research efforts aimed toward improved survival for neuro-aids patients. The survival disadvantage of women compared to men should be confirmed and the mechanisms underlying this disparity elucidated. Meanwhile, clinical and public health efforts might be directed towards screening, treatment, and support for women affected by neuro-aids, including potential assessment of comorbid psychiatric disorders. v

11 TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS ix xii xiv CHAPTER I INTRODUCTION 1 Overview of HIV/AIDS Epidemiology 1 Overview of Neurologic Manifestations of HIV/AIDS 2 Specific Aims 5 CHAPTER II LITERATURE REVIEW 6 PML Prognosis 6 PCNSL Prognosis 7 HAD Prognosis 9 Prognosis Following Cryptococcal Infection 9 Prognosis among Transfusion Recipients and Hemophiliacs 9 Research Combining Multiple Neuro-AIDS Conditions 10 Context for Prognostic Research 11 CHAPTER III RESEARCH DESIGN AND METHODS 12 Overview of Study Methods and Data Sources 12 HIV/AIDS Epidemiology in Iowa 14 Statewide Surveillance Database 14 Clinical Factors in Statewide Data 16 Demographic Factors in Statewide Data 17 Death Documentation in Statewide Data 17 Analysis of Statewide Cohort 18 Cohort Definition 18 Measures and Variables 18 Characterization of ART Use in the Statewide Cohort 22 Kaplan-Meier Analysis 23 Cox Regression Analysis 23 Power and Sample Size 25 Analysis of UIHC Cohort 25 UIHC Medical Record Data 25 HIV Program Data 26 State Health Registry of Iowa Data 26 UIHC Cohort Definition 27 UIHC Measures and Variables 28 Statistical Analyses 30 vi

12 CHAPTER IV RESULTS 31 Timing and Frequency of Neuro-AIDS Diagnoses and Rates of Death in the Statewide Cohort 31 Timing of Neuro-AIDS 31 Neuro-AIDS Diagnoses 35 Locations and Causes of Death 39 Characteristics of the Statewide Cohort and Patterns of ART Use within the Cohort 39 General Characteristics of ART Use in the Statewide Cohort 39 Availability of HIV Viral Load and CD4 Cell Data 45 Logistic Regression Model for Prediction of ART Use in the Statewide Cohort 48 Factors Influencing Survival among Persons in the Statewide Cohort 52 Timing of Neuro-AIDS 52 Neuro-AIDS Diagnoses 54 Other Potential Prognostic Factors 56 Impact of Clinical Factors on Survival among Persons with Neuro-AIDS 60 Impact of ART on Overall Survival 60 Interaction of ART and Treatment Era 62 Interactions of Age with ART and Neuro-AIDS Condition 71 Impact of Static Laboratory Values 72 Impact of Time-Varying Laboratory Values 72 Impact of Timing of Neuro-AIDS on Survival 78 Impact of Opportunistic Infections and Related Prophylactic Measures 78 Sensitivity of ART Effect to Exclusion of Zero-Length Survival Times 80 Sensitivity of ART Effect to Absent Information about Timing of Neuro-AIDS 80 Impact of Potential Factors in Healthcare Access and Disparity on Survival among Persons with Neuro-AIDS in the Statewide Cohort 81 Birth Sex and Survival 81 Sensitivity of Birth Sex Effect to Exclusion of the Longest Survival Times among Males 85 Secondary Assessment of Potential Factors in the Differential Survival of Men and Women in the Statewide Cohort 87 Race and Survival 89 Healthcare Facility Type and Survival 91 County of Residence and Survival 92 Marital Status and Survival 92 Comparative Analysis of Survival in a University-Based Cohort 96 Descriptive Summary of UIHC Cohort 96 Characteristics of ART Use in the UIHC Cohort 101 Summary of Survival Analyses for the UIHC Cohort 103 Impact of Clinical Factors on Survival in the UIHC Cohort 108 Impact of Sociodemographic Factors on Survival in the UIHC Cohort 111 vii

13 CHAPTER V DISCUSSION 113 Summary of Key Findings 113 Comparison of Cohorts 113 Patterns of ART Use 115 Impact of ART and HAART on Survival 116 Impact of Age and Neuro-AIDS Condition on Survival 118 Impact of Birth Sex on Survival 118 Impact of Other Clinical and Sociodemographic Factors 122 Modeling of Static and Time-Varying Laboratory Values 123 Study Strengths and Limitations 124 Context of Present Study in the Literature 127 Potential Directions for Subsequent Research, Public Health Practice, and Clinical Efforts 127 APPENDIX A DATA DICTIONARIES 131 APPENDIX B SUPPLEMENTARY TABLES 147 REFERENCES 168 viii

14 LIST OF TABLES Table 1.1 Burden of disease for select neuro-aids conditions 3 Table 3.1 Summary of selected variables collected by HARS/e-HARS 13 Table 3.2 Summary of data sources for the statewide and university-based cohorts 15 Table 4.1 Frequency of primary and secondary neuro-aids and mortality within the statewide cohort 32 Table 4.2 Frequency of single and mixed neuro-aids conditions and mortality within the statewide cohort 38 Table 4.3 Frequency of neuro-aids conditions and mortality within the statewide cohort 38 Table 4.4 Sociodemographic and clinical characteristics of the statewide cohort overall and stratified by reported ART use 40 Table 4.5 Univariate logistic regression results for prediction of ART use (n=230 unless otherwise noted) 49 Table 4.6 Multivariate logistic regression models for prediction of ART use, with and without laboratory values (n=230 unless otherwise noted) 51 Table 4.7 Median survival times for single and mixed neuro-aids conditions in the statewide cohort 57 Table 4.8 Median survival times stratified by ART use and ART era 59 Table 4.9 Cox regression model, controlling for clinical and sociodemographic variables, including timing of neuro-aids (primary versus secondary) (n=225) 63 Table 4.10 Cox regression model, controlling for clinical and sociodemographic variables, including timing of neuro-aids (using six-month cutoff) (n=225) 65 Table 4.11 Summary of stratification system for ART by treatment era model 68 Table 4.12 Stratified Cox regression model, accounting for interaction of ART and treatment era and timing of neuro-aids (primary versus secondary) (n=225) 69 Table 4.13 Summary of stratification system for age group by neuro-aids condition model 73 ix

15 Table 4.14 Stratified Cox regression model, accounting for the interaction of age group and neuro-aids condition and timing of neuro-aids (using six-month cutoff) (n=225) 74 Table 4.15 Estimates for ART use by treatment era after adjustment for static laboratory values 77 Table 4.16 Estimates for ART use by treatment era after adjustment for time-varying laboratory values 79 Table 4.17 Sociodemographic and clinical characteristics of the men and women in the statewide cohort (n=225) 82 Table 4.18 Adjustment of hazard ratio for birth sex by individual covariates (n=225) 86 Table 4.19 Comparison of measures of disease progression and severity, by birth sex 88 Table 4.20 Interaction of birth sex and county type (n=225) 90 Table 4.21 Crude and adjusted hazard ratios for county of residence at the time of AIDS diagnosis (n=225) 93 Table 4.22 Crude and adjusted hazard ratios for marital status (n=52) 95 Table 4.23 Frequency of initial neuro-aids conditions and mortality in the UIHC cohort 97 Table 4.24 Sociodemographic and clinical characteristics of the UIHC cohort overall and stratified by reported ART use 98 Table 4.25 Univariate logistic regression results for prediction of ART use in the UIHC cohort (n=172 unless otherwise noted) 102 Table 4.26 Multivariate logistic regression models for prediction of ART use in the UIHC cohort (n=172) 104 Table 4.27 Median survival times for single and mixed neuro-aids conditions in the UIHC cohort 105 Table 4.28 Accelerated failure time model for survival in the UIHC (n=171 for adjusted model) 109 Table A.1 Variables used in analysis of statewide cohort 131 Table A.2 Variables used in analysis of UIHC cohort 142 x

16 Table B.1 Stratified Cox regression model with ART by treatment era interaction and minimum CD4 count (n=139) 147 Table B.2 Stratified Cox regression model with age by neuro-aids condition interaction and minimum CD4 count (n=139) 149 Table B.3 Stratified Cox regression model with ART by treatment era interaction and time-varying CD4 count (n=127 observations) 152 Table B.4 Stratified Cox regression model with age by neuro-aids condition interaction and time-varying CD4 count (n=127 observations) 154 Table B.5 Cox regression model with ART by treatment interaction and PCP data (n=225) 157 Table B.6 Cox regression model with age by neuro-aids condition interaction and PCP data (n=225) 159 Table B.7 Stratified Cox regression model for records where survival time exceeds zero (n=215) 162 Table B.8 Stratified Cox regression model for records without timing of neuro-aids (n=225) 164 Table B.9 Accelerated failure time model for statewide cohort, accounting for interaction of ART and treatment era (n=215) 166 xi

17 LIST OF FIGURES Figure 4.1 Frequency of primary neuro-aids, by year of AIDS diagnosis ( ) 33 Figure 4.2 Frequency of secondary neuro-aids, by year of AIDS diagnosis ( ) 34 Figure 4.3 Frequency of neuro-aids within six months of the AIDS diagnosis, by year of AIDS diagnosis ( ) 36 Figure 4.4 Frequency of neuro-aids occurring more than six months after the AIDS diagnosis, by year of AIDS diagnosis ( ) 37 Figure 4.5 Frequency of cases with reported ART use by year of AIDS diagnosis ( ) 46 Figure 4.6 Frequency of cases without reported ART use by year of AIDS diagnosis ( ) 47 Figure 4.7 Kaplan-Meier curve for entire cohort for which survival time could be calculated (n=225) 53 Figure 4.8 Kaplan-Meier curves for primary and secondary neuro-aids 53 Figure 4.9 Kaplan-Meier curves for primary neuro-aids with one or two neuro-adcs and for secondary neuro-aids 54 Figure 4.10 Kaplan-Meier curves for neuro-aids within six-months of the AIDS diagnosis and later-onset neuro-aids 55 Figure 4.11 Kaplan-Meier curves for each of the five neuro-aids diagnoses and for a mixed neuro-aids category, representing more than one neuro-aids condition 55 Figure 4.12 Kaplan-Meier curves by era (pre-haart vs. HAART) 57 Figure 4.13 Kaplan-Meier curves by ART use 58 Figure 4.14 Kaplan-Meier curves stratified by both ART era and ART use 58 Figure 4.15 Kaplan-Meier curves by birth sex 61 Figure 4.16 Kaplan-Meier curves by age group (< 35 years vs. 35 years) 61 Figure 4.17 Kaplan-Meier survival curves for UIHC cohort (n=172) 105 Figure 4.18 Kaplan-Meier survival curves for UIHC cohort, by ART use 106 xii

18 Figure 4.19 Kaplan-Meier survival curves for UIHC cohort, by treatment era 107 Figure 4.20 Kaplan-Meier survival curves for UIHC cohort, by birth sex 107 Figure 4.21 Kaplan-Meier survival curves for UIHC cohort, by age group (<35 years vs. 35 years) 108 xiii

19 LIST OF ABBREVIATIONS ADC: AIDS-defining condition AIC: Akaike information criterion AIDS: Acquired immune deficiency syndrome ART: Antiretroviral therapy bdna: Branched deoxyribonucleic acid CD4: Cluster of differentiation 4 CHIC: Collaborative HIV Cohort (United Kingdom) CI: Confidence interval CMH: Cochran-Mantel-Haenszel CMV: Cytomegalovirus CNS: Central nervous system DRG: Diagnosis-related group e-hars: Enhanced HIV/AIDS Reporting System HAART: Highly active antiretroviral therapy HAD: HIV-associated dementia HARS: HIV/AIDS Reporting System HIV: Human immunodeficiency virus HMO: Health maintenance organization HR: Hazard ratio ICD: International Classification of Diseases IDPH: Iowa Department of Public Health IDU: Injection drug use JC virus: (A human polyomavirus) xiv

20 ml: Milliliter MSM: Male sexual contact with male N/H: Non-Hispanic NNRTI: Non-nucleoside reverse transcriptase inhibitor NRTI: Nucleoside reverse transcriptase inhibitor OI: Opportunistic infection OR: Odds ratio PCNSL: Primary central nervous system lymphoma PCP: Pneumocystis jiroveci pneumonia (formerly Pneumocystis carinii pneumonia) PML: Progressive multifocal leukoencephalopathy PS: Power and Sample Size Calculation RR: Risk ratio RT-PCR: Reverse transcriptase polymerase chain reaction SAS: Statistical Analysis Software SD: Standard deviation SEER: Surveillance Epidemiology and End Results SHRI: State Health Registry of Iowa STD: Sexually transmitted disease UIHC: University of Iowa Hospitals and Clinics US: United States L: Microliter xv

21 1 CHAPTER I INTRODUCTION Overview of HIV/AIDS Epidemiology Acquired immune deficiency syndrome (AIDS) was first identified in the United States (US) in Human immunodeficiency virus (HIV, a retrovirus) was subsequently identified as the etiologic agent of AIDS. The virus is most often spread by sexual contact, exposure to contaminated needles, perinatal transmission, or receipt of contaminated blood, blood products, or organs/tissues. Men with a history of sexual contact with men (MSM) comprise the majority of affected patients in the US, although injection drug use (IDU) and heterosexual contact, particularly that involving multiple sexual partners, are also important risk factors for HIV transmission. The general impact of HIV/AIDS has been widely reported: An estimated total of 33.4 million persons worldwide, including 1.4 million in North America, are living with HIV, while about 2.0 million persons (25,000 in North America) died of AIDS in 2008 [1]. Racial and ethnic minorities are disproportionately affected by HIV/AIDS, with African Americans comprising about 12% of the US population but about 46% of HIV infections in the US [2]. Meanwhile, while women currently represent a minority of HIV/AIDS patients, the proportion of AIDS cases affecting women has risen from 8% to 27% over a 20-year time span ( ) [3]. Over a period of years following initial HIV infection, depletion of immune cells, especially CD4-positive T cells, in affected patients leads to suppression of the immune system. This immunosuppression may, in turn, lead to the development of opportunistic infections and AIDS-related neoplasms, a number of which may involve the nervous system. Progression to AIDS may be defined by the presence of an AIDS-associated infection or

22 2 malignancy or, currently, by depletion of CD4-positive T cells to a cell count below 200 cells/ L. Antiretroviral therapies introduced around 1987 and highly active antiretroviral therapies introduced around 1996 have markedly improved the prognosis of AIDS patients, in general, and have delayed or prevented the progression of HIV to AIDS in many cases [4]. Overview of Neurologic Manifestations of HIV/AIDS Infection with HIV and progression to AIDS often result in neurologic and neuropsychiatric changes. In fact, progression from HIV to AIDS in a given patient may first be indicated by the diagnosis of an HIV/AIDS-related neurologic condition. Patients with HIV/AIDS are at risk for progressive multifocal leukoencephalopathy, central nervous system (CNS) tumors such as primary CNS lymphoma, and various CNS infections, including those caused by Cryptococcus, Toxoplasma, and cytomegalovirus. Patients may also develop distal sensory polyneuropathies, psychiatric comorbidities, or HIV-associated neurocognitive disorders, of which HIV-associated dementia constitutes the severest form. Notably, due to the prevalence of HIV/AIDS within younger populations, HIV-associated dementia has become the most common form of dementia among all young adults. The neurologic complications of HIV/AIDS generate a substantial burden of disease. Neurologic complications occur in approximately 40% of AIDS patients and in nearly 25% of HIV-infected persons who have not yet developed AIDS [5, 6]. Furthermore, AIDS-associated neurologic conditions (neuro-aids) often lead to severe disability and death and are associated with significantly higher mortality than non-neurologic AIDS [6]. Table 1.1 briefly describes five neurologic complications of HIV/AIDS, each of which predominantly impacts the central nervous system. Among these conditions are cryptococcal meningitis (cryptococcosis), toxoplasmal meningoencephalitis (toxoplasmosis),

23 3 Table 1.1 Burden of disease for select neuro-aids conditions [5, 7] Neuro-AIDS Condition Brief Description % Patients Affected Cryptococcosis Toxoplasmosis Progressive multifocal leukoencephalopathy (PML) Primary CNS lymphoma (PCNSL) HIV-associated dementia (HAD) A cause of meningitis in AIDS patients, etiologic agent is a yeast (Cryptococcus)* A cause of cerebral abscesses in AIDS patients, etiologic agent is a protozoan (Toxoplasma)* A demyelinating disorder that leads to multiple focal neurologic deficits, etiologic agent is a virus (JC virus)* A neoplastic condition affecting the brain and/ or spinal cord, associated with a virus (Epstein-Barr virus)* A cause of cognitive impairment in HIV-infected patients, etiology not well characterized 10% of AIDS patients, 3-5% as first AIDS-indicator disease 5-10% of AIDS patients, 3-4% as first AIDS-indicator disease 2-7% of all HIV-1 infected patients, 0.5-1% as first AIDS-indicator disease 1-4% of all HIV-1 infected patients, 0.5-1% as first AIDS-indicator disease Up to 60% of AIDS patients, 3-10% as first AIDS-indicator disease *Associated with a secondary or opportunistic infection in the presence of HIV-induced immunosuppression, type of secondary/ opportunistic agent noted

24 4 and progressive multifocal leukoencephalopathy (PML), all of which result from secondary, opportunistic infections following HIV-induced immunosuppression. Other neuro-aids conditions described in Table 1.1 include primary CNS lymphoma (PCNSL), a neoplastic condition affecting the CNS secondary to HIV-induced immunocompromise and itself associated with a secondary viral infection, and HIV-associated dementia (HAD). Table 1.1 summarizes the burden of disease for each of these five conditions within the general HIV/AIDS population. The evolution of treatment modalities over the course of the HIV/AIDS epidemic, including highly active antiretroviral therapy (HAART, introduced around 1996) and its pharmaceutical predecessor, antiretroviral therapy (ART, introduced around 1987), have influenced both the incidence and prognosis of neuro-aids [6, 8]. However, neurologic complications may either precede the initiation of HAART in individual patients or may develop in the presence of HAART. Thus, while some of the most severe neuro-aids conditions have become less common in the HAART era, many such conditions still occur, and neurologic complications remain a significant cause of morbidity and mortality in the HIV/AIDS population [9, 10]. Despite the impact of neurologic complications on the HIV/AIDS population, the prognostic data available to affected patients and their healthcare providers has been limited. The objective of the present study was to better characterize the patterns and predictors of survival, including the impacts of ART use and potential factors in healthcare access and disparity, among patients with one or more of the five neuro-aids conditions listed in Table 1.1, over a nearly three-decade period representing the course of the HIV/AIDS epidemic in Iowa. The findings from this research provide an expanded prognostic context for the neuro-aids epidemic and augment previous reports in this area of interest.

25 5 Specific Aims For the present study, a retrospective cohort was drawn from the statewide HIV/AIDS reporting system, which encompasses data from 1982 to present. A universitybased retrospective cohort was then used to validate and extend the analyses conducted for the statewide sample. The specific aims of the study were as follows: Specific Aim 1. To assess the prognostic impact of clinical factors among patients with neuro- AIDS, using statewide cohort data Hypothesis 1a. Among patients with neuro-aids, the probability of survival will be higher (and survival time increased) among patients receiving ART, controlling for other factors, such as neuro-aids condition, age, race, birth sex, healthcare facility type, CD4 cell count, HIV viral load, year of diagnosis, other opportunistic infections and AIDS-related neoplasms, and select behavioral risk factors, using a Cox regression model. Hypothesis 1b. The effect of ART use will vary over time, i.e., as newer and more effective drugs became available, as measured by the interaction of ART use and year of diagnosis in this model. Hypothesis 1c. Patient age will interact with ART use and neuro-aids condition in this model. Specific Aim 2. To ascertain the prognostic impact of potential factors in healthcare access and disparity birth sex, race, rural/urban county status, healthcare facility type, and marital status on survival within the statewide cohort, using a Cox regression model Specific Aim 3. To validate and extend the findings from the statewide cohort with a university-based cohort, for which more detailed clinical information is available

26 6 CHAPTER II LITERATURE REVIEW Relatively few studies of survival among patients with neuro-aids have been conducted, and the scope of available evidence is limited, almost entirely, to just two neuro- AIDS conditions PML and PCNSL. Furthermore, populations represented in previous studies include international cohorts, Medicare beneficiaries, and transfusion recipients/ hemophiliacs, none of which may accurately reflect the general population of HIV/AIDS patients in the US. PML Prognosis Most prognostic studies involving PML have focused either on survival among patients receiving HAART or on the prognostic impact of HAART compared to other therapeutic strategies. In a US cohort of HAART recipients with PML (n=25), survival was improved with HAART (median survival= 46.4 weeks among HAART patients compared with 10.6 weeks among a historical comparison group) and was associated with indicators of HAART responsiveness (i.e., decreasing viral load and increasing CD4 cell count) [11]. The analyses were restricted to Kaplan-Meier methods and did not employ Cox regression modeling to adjust for potential confounders or other covariates. Similarly, prognostic patterns before and after the institution of HAART could not be deduced from these results. A retrospective cohort in Germany compared 29 patients with PML 5 who received HAART, 10 who received a different antiretroviral drug regimen (nucleoside analogues only), and 14 who received no therapy or stopped therapy altogether after PML was identified [12]. The HAART group (median survival >500 days) had a significantly better prognosis than the other two groups combined (p< by log-rank test). In this case, Cox regression methods were used, and younger age was the only additional predictor

27 7 of survival. Other covariates, including gender, CD4 cell count, HIV transmission route, and other therapies, were not significant predictors in the model. Of the 29 patients in this cohort, however, 25 were male and only 5 received HAART. Meanwhile, an Italian cohort study of PML found that, while PML incidence may not differ with HAART use, post-pml prognosis does improve with the use of these drugs (survival=245 days with HAART vs. 66 days without HAART) [13]. In this case, both younger age and higher CD4 count were positive prognostic predictors for patients with PML. Another report from the same cohort a prospective study initiated in 2000 demonstrated that advanced age, low CD4 cell count, AIDS prior to PML, and signs/ symptoms such as altered mental status were predictors of poor survival in a multivariate Cox regression model (n=101 persons with PML), while HAART had a significant, positive effect on survival [14]. Interestingly, although 20% of the PML patients in this study were women, sex was not included in the multivariate model. Finally, similar results were reported for a French cohort of PML patients ( , n=81) [15]. In a multivariate Cox regression model, older age, increased extent of disease-related disability, and lower CD4 count were poor prognostic indicators, while combination ART use after the PML diagnosis had a positive impact on survival. Again, sex was not included in the multivariate model, as it was not a statistically significant prognostic indicator in a univariate modeling procedure. PCNSL Prognosis A case series conducted at the University of California San Diego showed improved survival in PCNSL patients receiving a multimodal oncologic therapy (median survival=13.5 months vs. an expected 2-5 months in patients treated with whole brain irradiation alone, total n=4) [16]. As the study author notes, however, most HIV-positive PCNSL patients

28 8 have risk profiles that would reduce their ability to tolerate multimodal treatment and, therefore, would not be eligible for this therapy at all. A US cohort study of PCNSL patients linked data from Medicare and Surveillance Epidemiology and End Results (SEER) and found that the median survival time among these patients was about two months and that neither age category (dichotomized as greater than or less than 50 years) nor time of diagnosis (before or after 1996) affected survival in these patients [17]. Again, Cox regression modeling was not described. Although this cohort was restricted to Medicare beneficiaries, the median age in the study was 41 years, since some AIDS patients qualify for Medicare under disability-associated eligibility requirements. The study authors acknowledged that such patients may not be representative of the general population with HIV/AIDS or with AIDS-related PCNSL. A more recently published analysis of SEER data from nine sites in the US, including Iowa, demonstrated that HIV-positive cases of PCNSL from 1973 to 2004 were associated with significantly worse outcomes than HIV-negative cases (median survival=2 months and 12 months for HIV-positive and HIV-negative cases, respectively) [18]. For all cases combined (HIV-positive and HIV-negative), younger age, later year of diagnosis, female sex, radiation therapy, and marital status (married vs. other) were all positive prognostic indicators, while race did not appear to influence survival. Finally, a University of Miami study comparing HIV-positive and HIV-negative cases of PCNSL between 1999 and 2008 also found that HIV-positive status was a very poor prognostic indicator [19]. Of 35 cases with HIV-associated PCNSL, 13 (35%) had a history of HAART use prior to the PCNSL diagnosis. Among those without a history of HAART, initiation of HAART after the PCNSL diagnosis appeared to exert a significant protective

29 9 effect (median survival=4.0 months vs. 1.1 months among those for whom HAART was not initiated). HAD Prognosis An Italian cohort study of HIV-associated dementia found that age was not a significant predictor of survival, controlling for gender, viral load, CD4 cell count, HAART use, and other AIDS-associated diseases [20]. However, these authors observed that HAART use may buffer the risk of HAD development (that is, incidence of HAD) and may thus confer a specific neuroprotective effect in older patients. Prognosis Following Cryptococcal Infection A study conducted in South India briefly characterized survival among patients with cryptococcal infections of the CNS, although only 18 of 60 cases were HIV-related [21]. Among these patients, survival tended to be worse among HIV-related cases (p=0.05), but the specific predictors of survival among these cases were unclear. Prognosis among Transfusion Recipients and Hemophiliacs Another Italian study employed surveillance data to characterize survival among patients who contracted HIV from contaminated blood products (i.e., among transfusion recipients and hemophiliacs, with nearly 40% of cases > 40 years old and 77% male) [22]. These authors found that male gender, age (35 years or older), geographic region (south Italy), low CD4 cell count, and longer time since AIDS diagnosis were associated with poor survival and that neurologic complications of infection were a negative prognostic indicator (median survival with a neurologic condition=4.9 months). Interestingly, these authors note that the presence of neurologic complications modified the effect of age on survival,

30 10 suggesting that the impact of age as a prognostic indicator in various neuro-aids conditions should be further explored. Research Combining Multiple Neuro-AIDS Conditions The United Kingdom Collaborative HIV Cohort (CHIC) study recently reported incidence and survival data for the period of for four neuro-aids conditions PML, HAD (HIV encephalopathy), cryptococcosis, and toxoplasmosis (total n=574) [9]. Although the incidence of these four conditions tended to decline over the course of the study period, this study highlights the continued development of neuro-aids diagnoses in the recent era of antiretroviral therapies. In fact, the reported prevalence of ART use prior to the neuro-aids diagnosis ranged from 41.9% for toxoplasmosis to 66.3% for HAD; for HAART specifically, this ranged from 33.2% for toxoplasmosis to 47.8% for PML. The CHIC study also highlights the poor overall survival of patients with neuro- AIDS in the HAART era, with rates of death ranging from 27.9% for cryptococcosis (median follow-up time=3.8 years) to 52.2% for PML (median follow-up time=0.7 years). Earlier year of diagnosis (i.e., 1996/1997), previous AIDS illness, PML, and older age were significantly associated with poor survival in a multivariate Cox regression model controlling for neuro-aids diagnosis, year of diagnosis, race/ethnicity, and select risk factors. Treatment prior to the neuro-aids diagnosis exhibited no significant impact on survival in a univariate model (HR=1.19, 95% CI: 0.90 to 1.58) and was excluded from the multivariate procedure. Similarly, female gender was associated with a non-significant survival advantage in a univariate procedure (HR=0.73, 95% CI: 0.50 to 1.04, compared to males) but was not included in the multivariate model, despite evidence elsewhere in the study that women were significantly less likely to develop one of the four conditions (RR=0.73, 95% CI: 0.58 to 0.92 from a multivariate Poisson model including year of diagnosis, age, race/ethnicity, risk

31 11 group, CD4 cell count, and HIV viral load) and that the proportion of males in the overall cohort (from which the neuro-aids conditions were selected) dropped from 84.5% to 76.3% over the course of the study period. Context for Prognostic Research Effective clinical management of neuro-aids conditions relies on the availability and quality of existing epidemiologic evidence and on resulting evidence-based standards of care. Thorough assessment of the patterns and predictors of neuro-aids prognosis may help to promote effective allocation of healthcare resources to those patients at highest risk, guide development of future intervention strategies aimed toward improving prognosis in patients with neuro-aids, and elucidate the physiologic mechanisms mediating survival in these patients, so as to inform the broader understanding of neuro-aids pathologies and targeted therapeutic approaches. Given the practical limitations of studying certain neurologic diseases (e.g., the invasiveness of direct neuropathologic examinations, such as lumbar puncture and brain biopsy), effective use of clinical and epidemiologic data to understand neuro-aids pathology and alleviate its effects should be widely encouraged. Yet, few studies have investigated survival by incorporating multiple neurologic conditions and/or sociodemographic factors, robust Cox regression modeling procedures, and long-term surveillance data. Here, such an investigation was conducted, using statewide surveillance data for neuro-aids cases reported over the course of the HIV/AIDS epidemic in Iowa.

32 12 CHAPTER III RESEARCH DESIGN AND METHODS Overview of Study Methods and Data Sources HIV/AIDS surveillance and follow-up data from the Iowa Department of Public Health (IDPH) were used to model survival among patients with one or more neuro-aids condition (from those previously listed in Table 1.1), using both Kaplan-Meier analysis and Cox regression. The modeling procedures were used to estimate the effect of ART use and sociodemographic factors on survival, as well as the potential interaction of age with ART use and individual neuro-aids conditions in the statewide cohort. Sociodemographic and clinical factors associated with ART use were also identified. Similar analyses were then conducted using more detailed clinical data from two sources at the University of Iowa Hospitals and Clinics (UIHC), for comparison with statewide results. (For both cohorts, ART includes both ART and HAART unless otherwise noted.) To maximize ascertainment of PCNSL cases and the primary endpoint for all records in the UIHC cohort, linkage with death certificate data and cancer incidence data maintained by the State Health Registry of Iowa (SHRI) was also used. Due to privacy restrictions on the use and linkage of HIV/AIDS data, the IDPH cohort and UIHC cohort were analyzed separately. A summary of data sources is given in Table 3.1. All data use was approved by the relevant organizations, including the Iowa Department of Public Health and the University of Iowa Institutional Review Board. Combining Iowa data with similar data from another state was considered, as a means for increasing the total sample size. However, selection of a comparable state would have been complicated by the need to identify a state where surveillance requirements have

33 13 Table 3.1 Summary of data sources for the statewide and university-based cohorts Data Type Source Organization Research Use HIV/AIDS Surveillance Data (HARS/e-HARS) Iowa Department of Public Health, Bureau of HIV, STD, and Hepatitis Primary cohort, clinical information and endpoint ascertainment UI HIV Program Data UI HIV Program University-based cohort, clinical information UIHC Medical Record UIHC Joint Office for Compliance University-based cohort, clinical information Death Certificate Data State Health Registry of Iowa University-based cohort, PCNSL information and endpoint ascertainment HARS/e-HARS=HIV/AIDS Reporting System and enhanced HIV/AIDS Reporting System

34 14 been similar to those used in Iowa over the course of the study period. (For instance, uniform HIV infection reporting was not achieved for all states until April 2008, and testing strategies still vary from state to state [23].) Furthermore, data security and access requirements in additional states would almost certainly have created substantial delays in research progress. Finally, the use of Iowa surveillance data allowed for comparison with an Iowa-based university cohort, as well as assessment of rural/urban county status as a potential prognostic factor, and represents a method which may be replicated elsewhere in states with similar surveillance data. HIV/AIDS Epidemiology in Iowa HIV/ AIDS surveillance data for the state of Iowa are collected via passive surveillance by the IDPH Bureau of HIV, STD, and Hepatitis. Physicians, hospitals, and laboratories must report cases of HIV/AIDS, deaths associated with HIV/AIDS, potential perinatal exposures to HIV, and HIV-related laboratory data, including viral detection assays, culture results, CD4 cell counts, and HIV viral loads within seven days of the reportable event [24]. Data from such reports and from follow-up investigations conducted by IDPH staff are contained in the statewide Enhanced HIV/AIDS Reporting System (e-hars). Statewide Surveillance Database E-HARS is, in fact, a nationwide surveillance system, involving data collection in all 50 states, and is an application for collecting, storing, and retrieving the data the Centers for Disease Control and Prevention (CDC) has identified as necessary to monitor the epidemic and evaluate HIV prevention policies and programs [Walker, IDPH, personal communication, 03/25/2009 via Dr. Jeffery Meier]. The e-hars database encompasses a considerable array of demographic, clinical, laboratory, and risk factor data for HIV/AIDS patients in each participating state. Select e-hars variables are listed in Table 3.2. While

35 15 Table 3.2 Summary of selected variables collected by HARS/e-HARS Sociodemographic Measures Healthcare/ Treatment Clinical/ Laboratory Birth sex Race/ethnicity Age at AIDS diagnosis County of residence at AIDS diagnosis Marital status (primarily ascertained at time of death) Measures Healthcare facility at AIDS diagnosis ART use* Pneumocystis pneumonia prophylaxis* Enrollment in clinical trial Measures HIV diagnostic assay (type and date) Date of AIDS diagnosis CD4 cell count (multiple measures possible) HIV viral load (multiple measures possible) Other Measures Known risk factors for HIV exposure [male sexual contact with male (MSM), injection drug use (IDU), history of blood transfusion, receipt of clotting factor for hemophilia, etc.] Opportunistic infections and AIDS-related neoplasms (type* and date) *Recorded in database if present, presumed absent otherwise for all persons in the database; Note: Data for all variables in this table were complete (n=230) for the statewide cohort described below, except for marital status (n=53, 23.0% complete), enrollment in clinical trial (n=70, 30.4% complete), HIV diagnostic assay (n=224, 97.4% with type; n=147/224, 65.6% with complete month and year), CD4 cell count (n=144, 62.6% with at least one value), and HIV viral load (n=74, 32.2% with at least one value). Dates for individual opportunistic infections are also largely complete a representative count using the neuro- AIDS conditions in this study gives n=225 (97.8%) with complete month and year.

36 16 this enhanced reporting system was unveiled somewhat recently (being implemented in Iowa in 2008), this database incorporates historical surveillance data derived from earlier versions of the system (the AIDS Reporting System and the HIV/AIDS Reporting System, HARS). The HARS/e-HARS database offers the most complete, population-based representation of HIV/AIDS cases in Iowa over the course of the epidemic. The CDC estimates that reporting of AIDS cases is more than 85% complete for most US regions and that death reporting for known AIDS cases is more than 90% complete [25]. The current database maintained by IDPH contains HARS legacy documents from 1982 through early 2008 and e-hars records developed between 2008 and As of 2009, the system included records for 4,286 persons including 1,158 persons with HIV infection which had not yet progressed to AIDS, another 2,993 with AIDS, and additional records pertaining to perinatally exposed infants and children [Harms, IDPH, personal communication, 09/03/2009 and 10/02/2009]. Clinical Factors in Statewide Data The Iowa HARS/e-HARS database contains records for all five of the neuro-aids conditions in Table 1.1, allowing these conditions to be studied on a population-wide basis alongside clinical data, including information about the HIV/AIDS diagnosis, neuro-aids conditions, and other opportunistic infections or AIDS-associated neoplasms. ART use and Pneumocystis pneumonia (PCP) prophylaxis at the time of the report are also recorded, and CD4 cell count and viral load data are available for a subset of cases. Since the database spans the entire HIV/AIDS epidemic in Iowa, reported ART use can be broadly classified as occurring during the pre-haart or HAART era, and changes in ART-associated prognosis over time can also be assessed.