HEALTHCARE UTILIZATION AMONG PERSONS LIVING WITH HIV
WITH ATTENTION TO THE INFLUENCES OF HEPATITIS
CO-INFECTION AND ELITE CONTROL
by
Trevor Adam Crowell, M.D.
A dissertation submitted to Johns Hopkins University in conformity with the
requirements for the degree of Doctor of Philosophy
Baltimore, Maryland
January, 2015
© 2015 Trevor A. Crowell
All Rights Reserved
ABSTRACT
HIV infection has evolved from a consistently fatal diagnosis into a chronic
condition that requires lifelong medication and care. These do not come cheaply. In 2015,
the United States government is expected to spend $17.5 billion on health care services
and treatment for persons living with HIV (PLWH). PLWH are living longer than they
were earlier in the epidemic and beginning to experience age-related complications of
comorbidities, such as viral hepatitis. Chronic co-infection with hepatitis B virus and/or
hepatitis C virus is common among PLWH and plays an increasingly important role in
the morbidity and mortality observed in this population. Understanding its impact on
healthcare utilization can help to inform the allocation of limited healthcare resources,
improve the cost-effectiveness of HIV care, and guide clinical decision-making.
Understanding factors associated with healthcare utilization and costs has become
especially important as the Patient Protection and Affordable Care Act (ACA) is poised
to dramatically alter the way healthcare is delivered in the United States. Optimism about
improved access to care as a result of the ACA is mirrored by newfound optimism about
the possibility of someday developing interventions to achieve a functional cure of HIV,
or HIV remission. Elite controllers are a unique and rare subset of PLWH that
demonstrate spontaneous virologic control without a need for antiretroviral therapy
(ART). In these patients, investigation of healthcare utilization provides insight into the
clinical outcomes of elite control that may then inform not only our understanding of
costs of care associated with elite control, but also the wisdom of trying to induce a state
similar to elite control as a means of achieving HIV remission.
ii
The research presented here explores healthcare utilization among PLWH with
particular emphasis on the influences of hepatitis co-infection and elite control. This
research uses data collected by the HIV Research Network (HIVRN), a consortium of
specialty HIV care clinics in 11 cities across the United States.
The first study investigates the potential role of hepatitis co-infection as a risk
factor for hospitalization among adult patients receiving longitudinal HIV care at nine
clinical care sites. In 2010, a total of 2,793 hospitalizations were observed among 12,819
patients. In this study, PLWH who were co-infected with hepatitis B and/or hepatitis C
had hospitalization rates that were about 50% higher than those seen among persons with
HIV mono-infection, after adjusting for factors such as age, CD4 count and HIV viral
load. Hospitalization rates for non-AIDS-defining infections were almost twice as high
among PLWH with any hepatitis co-infection as compared to those with HIV monoinfection. Hepatitis B co-infection was associated with more hospitalizations for
gastrointestinal/liver disease and hepatitis C co-infection was associated with more
hospitalizations for psychiatric illnesses. Since hospitalization is a particularly costly
form of healthcare utilization, it is important to identify interventions that may attenuate
the risk of hospitalization among PLWH with hepatitis co-infection. Policy-makers and
third-party payors should be aware of the higher hospitalization rates associated with
hepatitis co-infection when allocating healthcare resources and considering models of
healthcare delivery.
The second study investigates the associations between hepatitis co-infection and
utilization of primary HIV care, mental health, and hospital services at four sites from
2006-2011. Outpatient HIV visits did not differ by hepatitis serostatus and decreased over
iii
time, likely reflecting evolving professional society guidelines that recommend less
frequent monitoring for patients with well-controlled HIV. Mental health visits were
more common among HIV/HCV co-infected persons than among HIV mono-infected
persons, emphasizing the important role of psychiatric disease in this population. As
observed in the first study, hospitalization rates were higher among all hepatitis-infected
groups than in the HIV mono-infected group. Importantly, this observation has not
changed over time. With safer, more effective therapies for hepatitis C becoming
available, it will be important to determine whether use of these therapies decreases
hospitalization risk. If so, this may provide an important counterbalance to the high cost
of these medications. Again, these observations can critically inform the decisions of
policy-makers and third-party payors in the setting of an evolving United States
healthcare system.
The third study investigates hospitalization rates among elite controllers as
compared to persons with medically controlled HIV at 11 sites from 2005-2011. With
149 elite controllers, the data from this study represent one of the largest published
cohorts. After adjustment for demographic and clinical factors, elite control was
associated with higher rates of all-cause (adjusted incidence rate ratio 1.77 [95% CI 1.212.60]), cardiovascular (3.19 [1.50-6.79]) and psychiatric (3.98 [1.54-10.28])
hospitalization than was medical control. Hospitalizations for cardiovascular disease were
disproportionately common among elite controllers. These findings represent some of the
first data on clinical outcomes in this population and are consistent with prior studies
demonstrating high rates of inflammation and a high burden of apparent cardiovascular
disease on radiographic screening. Care providers may consider these findings when
iv
deciding whether or not to initiate ART in patients with elite control. These data also
suggest that elite control may not be an ideal model for the functional cure of HIV, since
patients treated with ART appear to have better outcomes in terms of hospitalization
These studies have each been published in medical journals and are reprinted here
with permission. They provide insights that can guide the clinical care of PLWH who are
co-infected with hepatitis or who demonstrate elite control. They also improve our
understanding of factors related to healthcare utilization and, therefore, costs of HIV care.
At a time when access to care is expanding, it is essential to understand and manage costs
of care. At a time when functional cure of HIV has become a realistic goal, it is essential
to understand precisely the goal we wish to achieve.
v
THESIS COMMITTEE
Research Mentor
Kelly A. Gebo, M.D., M.P.H.
Academic Advisor
David Newman-Toker, M.D., Ph.D.
Readers
Stephen J. Gange, Ph.D. (Thesis Committee Chair)
Marie Diener-West, Ph.D.
vi
PREFACE
This dissertation is the culmination of my work as both a graduate student and a
fellow in infectious diseases at Johns Hopkins University. I am tremendously grateful to
the people who made this exceptional combination of educational experiences possible.
As my research mentor, Kelly Gebo provided me with outstanding opportunities to apply
my analytic skills to projects that complemented my coursework. She provided feedback
and guidance that has doubtlessly made me a better investigator. Stephen Berry provided
me with my first glimpses at the code used to conduct statistical analyses and followed
that with detailed and insightful guidance as I developed my own analyses. His editorial
comments on my manuscripts have made me a much better scientific writer. Throughout
my training and research, I have benefitted from the wisdom of colleagues and teachers
including Stuart Ray, Khalil Ghanem, Yuka Manabe, Joel Blankson, Joel Gallant,
Stephen Gange, David Newman-Toker, Jonathan Zenilman and countless others.
My participation in the Graduate Training Program in Clinical Investigation was
made possible through funding mechanisms that remain mysterious to me. I know that I
am forever grateful to Charlie Flexner and Franklin Adkinson for giving me this
opportunity. Through this program, I have developed a skill set that enables me to
conduct exciting and rewarding work. The program connected me to a wonderful
network of classmates who have provided essential intellectual and emotional support.
For such support, I am particularly indebted to Emily McGowan and Allison Lambert.
I dedicate this work to Thea, who nurtured my intellectual curiosity and always
believed I could do great things.
vii
TABLE OF CONTENTS
Abstract ............................................................................................................................................ ii
Thesis Committee ........................................................................................................................... vi
Preface ........................................................................................................................................... vii
Table of Contents .......................................................................................................................... viii
List of Tables ................................................................................................................................... x
List of Figures ................................................................................................................................. xi
Chapter 1: Introduction ................................................................................................................... 1
HIV: Early Reports and Outcomes of a New Infectious Disease ................................................ 2
Antiretroviral Therapy and the Hope for Survival ....................................................................... 3
The Growing Importance of Hepatitis Co-Infection among Persons Living with HIV ............... 6
Failures of Healthcare in the United States for Persons Living with HIV ................................... 8
Changes in Healthcare in the United States for Persons Living with HIV ................................ 11
Costs of Healthcare among Persons Living with HIV ............................................................... 13
Elite Control and the Promise of HIV Remission ...................................................................... 16
Specific Aims ............................................................................................................................. 18
References .................................................................................................................................. 22
Chapter 2: Impact of Hepatitis Co-Infection on Hospitalization Rates and Causes in a MultiCenter Cohort of Persons Living with HIV ................................................................................... 35
Abstract ...................................................................................................................................... 36
Background ................................................................................................................................ 38
Methods ..................................................................................................................................... 39
Results........................................................................................................................................ 43
Discussion .................................................................................................................................. 46
Acknowledgments...................................................................................................................... 50
References .................................................................................................................................. 52
Chapter 3: Impact of Hepatitis Co-Infection on Healthcare Utilization among Persons Living
with HIV ........................................................................................................................................ 63
Abstract ...................................................................................................................................... 64
Introduction ................................................................................................................................ 65
Methods ..................................................................................................................................... 65
Results........................................................................................................................................ 68
viii
Discussion .................................................................................................................................. 71
Acknowledgments...................................................................................................................... 74
References .................................................................................................................................. 76
Chapter 4: Hospitalization Rates and Reasons among HIV Elite Controllers and Persons With
Medically Controlled HIV Infection .............................................................................................. 87
Abstract ...................................................................................................................................... 88
Background ................................................................................................................................ 89
Methods ..................................................................................................................................... 89
Results........................................................................................................................................ 94
Discussion .................................................................................................................................. 97
Acknowledgments.................................................................................................................... 103
References ................................................................................................................................ 105
Chapter 5: Conclusion................................................................................................................. 118
References ................................................................................................................................ 127
Curriculum Vitae ......................................................................................................................... 131
ix
LIST OF TABLES
Table 2-1: Study Population Characteristics Stratified by Hepatitis Serostatus ............... 55
Table 2-2: Univariate and Multivariate Analyses of Risk Factors for All-Cause
Hospitalization .................................................................................................................. 57
Table 2-3: Most Common Individual Diagnoses Within Diagnostic Categories ............. 59
Table 3-1: Population Demographic and Clinical Characteristics at Study Entry............ 79
Table 3-2: Multivariable Analysis of Risk Factors for Healthcare Utilization ................. 81
Table 3-3: Healthcare Utilization Rates by Hepatitis Serostatus. ..................................... 83
Table 4-1: Demographic and Clinical Characteristics at Study Entry by HIV Control
Status ............................................................................................................................... 109
Table 4-2: Univariable and Multivariable Analyses of Factors Associated with All-Cause
Hospitalization ................................................................................................................ 112
Table 4-3: Hospitalizations by Diagnostic Category at Nine Sites with ICD-9 Data ..... 114
Table 4-4: Multivariable Analyses of Factors Associated with Cause-Specific
Hospitalization at Nine Sites with ICD-9 Data ............................................................... 115
x
LIST OF FIGURES
Figure 2-1: Unadjusted Hospitalization Rates by Diagnostic Category ........................... 61
Figure 2-2: Adjusted Relative Risk of Hospitalization by Diagnostic Category. ............. 62
Figure 3-1: Healthcare Utilization Rates by Hepatitis Serostatus. ................................... 86
Figure 4-1: Unadjusted All-Cause Hospitalization Rates by HIV Control Status .......... 117
xi
CHAPTER 1:
INTRODUCTION
1
HIV: EARLY REPORTS AND OUTCOMES OF A NEW INFECTIOUS DISEASE
On June 4, 1981, a report of five unusual cases of Pneumocystic pneumonia in
previously healthy homosexual men in Los Angeles became the first description of what
would later be called acquired immune deficiency syndrome (AIDS).1 Soon thereafter,
similar cases were described among homosexual men in New York and other cities,
injection drug users, Haitian immigrants and hemophiliacs.2-7 By 1983, scientists
identified the causative agent of AIDS, a retrovirus that would eventually be named the
human immunodeficiency virus (HIV).8 From these first few clusters of cases, the disease
spread quickly. By the beginning of 1985, studies demonstrated HIV seroprevalence rates
of 35%-65% among homosexual men in the United States,9-11 87% among injection drug
users presenting for rehabilitation in New York City,12 and 56% among persons with
hemophilia A presenting for antihemophilic factor concentrates.13 HIV/AIDS quickly
emerged as one of the most significant infectious diseases in the United States and
worldwide.
In addition to being notable for its rapid spread, particularly within specific
populations, HIV/AIDS was also immediately remarkable for its morbidity and mortality.
In addition to Pneumocystic pneumonia, clinical manifestations recognized among those
first cases of HIV/AIDS included Kaposi sarcoma, non-Hodgkin lymphoma, oral and
esophageal Candidiasis, central nervous system Toxoplasmosis, progressive herpes
simplex virus, chronic enteric coccidiosis, and other opportunistic infections.5-7,14 The
disease was almost uniformly fatal, with five-year mortality rates of 85% or more in
various early reports.15-17 The disease had no specific treatment and no cure.
2
ANTIRETROVIRAL THERAPY AND THE HOPE FOR SURVIVAL
On March 19, 1987, the U.S. Food and Drug Administration (FDA) approved the
first antiretroviral drug, zidovudine (AZT).18 By inhibiting the viral reverse transcriptase,
AZT halts viral replication and prevents infection of new cells.19,20 Studies showed that
therapy with AZT caused a rapid decline in HIV viral load in the peripheral blood, as
well as weight gain, improved cognition, immune reconstitution and substantial
reductions in mortality.21-24 However, optimism about this new drug was tempered by the
sometimes severe side effects that could include rash, nausea, headache, and, most
importantly, dose-limiting bone marrow suppression.24-27 It also quickly became apparent
that the benefits of AZT monotherapy were short-lived. Within 6-12 months of starting
therapy, peripheral viral loads rebounded, CD4 counts returned to pre-treatment levels,
and clinical evidence of immune reconstitution reversed.23,28-30 The rapid development of
genotypic drug resistance severely limited the long-term efficacy of AZT and other early
antiretroviral monotherapies.31-35 Once resistance developed, it was irreversible.36-40
The solution to the problem of drug resistance came with combination
antiretroviral therapies. By combining drugs with different mechanisms of action, viral
replication could be suppressed below the levels achieved with monotherapy alone.41-43
With less viral replication there was less viral evolution and, therefore, less development
of drug-resistant variants.42 Multidrug regimens slowed disease progression and
prolonged life.43-46 Three-drug therapy, generally consisting of drugs from at least two
different classes and termed highly active antiretroviral therapy (HAART), reduced HIV1 replication, increased CD4 cell counts, and decreased serum inflammatory markers
more than prior one- and two-drug regimens.45,46 More importantly, these improvements
3
appeared to persist indefinitely. When HAART became widely available in the United
States in 1996, potent and lasting control of HIV became a realistic possibility for over
200,000 Americans living with HIV; this came too late for the 343,000 Americans who
had already died of the disease.47
The impact of HAART on the HIV epidemic was swift and dramatic. In 1995,
there were 49,985 deaths among persons with AIDS. In 1997, this number fell by over
56% to 21,909 deaths.48 In 1995, HIV was the leading cause of death among people 25 to
44 years old in the United States; In 1997, it was the fifth leading cause of death.49 Ageadjusted death rates directly attributable to HIV dropped 47.7% from 1996 to 1997
alone.49 In the HIV Outpatient Study (HOPS) cohort of clinics specializing in HIV care,
mortality declined from 29.4 deaths per 100 person-years in the first quarter of 1995 to
8.8 deaths per 100 person-years in the second quarter of 1997.50 In 1994, the combined
incidence of Pneumocystic pneumonia, Mycobacterium avium complex disease and
cytomegalovirus retinitis in this cohort was 21.9 per 100 person-years, and by mid-1997
it was only 3.7 per 100 person-years.50
With effective therapy for HIV finally a reality, focus shifted to the development
of antiretroviral drugs that were easier to use and had fewer side effects. In 1996,
HAART regimens consisted of three drugs given 2-3 times per day.45,46 Side effects
included moderate-to-severe fatigue, nausea, diarrhea, kidney stones, liver function
abnormalities and bone marrow toxicities.45,46 With prolonged use, patients began to
present with abnormal redistribution of body fat, elevated cholesterol, insulin resistance
and elevated rates of cardiovascular disease.51-54 The drugs were so difficult to take and
tolerate that after 8 months of use, only 60% of patients reported complete adherence to
4
their regimen.55 Prolonged treatment interruptions, or “drug holidays,” were investigated
in a number of studies that documented swift viral rebound upon HAART cessation.56-58
Shorter treatment interruptions, such as alternating weeks on and off therapy, also failed
to continuously suppress viremia.59 Researchers also tried interruptions tied to CD4
counts, with therapy held at high CD4 counts and resumed when CD4 dropped below a
certain threshold. This strategy demonstrated reasonable short-term safety with high CD4
thresholds for resumption of therapy and close monitoring, but increased risk of severe
complications of HIV and death with lower CD4 thresholds and longer follow-up
intervals.60-62 Ultimately, it became clear that continuous and lifelong therapy was the
only realistic option for keeping the virus safely at bay.
Fortunately, in the nearly two decades since HAART became widely available in
the United States, incredible advances have been made in antiretroviral drug development.
In 1996, nine medications were FDA-approved for the treatment of HIV. In 2014, there
are 33 FDA-approved medications for the treatment of HIV, including four multidrug
one-pill-once-a-day regimens.63 Single tablet regimens in use today are more potent and
have fewer side effects than regimens from earlier in the epidemic that contained nine or
more pills. The term “highly active antiretroviral therapy” has even been replaced simply
by antiretroviral therapy (ART), because essentially all therapies in use today are
considered highly active. Concerns about limiting antiretroviral exposure in order to
minimize toxicities have gradually been replaced by mandates to start therapy earlier and
earlier in the course of infection.64,65
5
THE GROWING IMPORTANCE OF HEPATITIS CO-INFECTION AMONG
PERSONS LIVING WITH HIV
Since ART became widely available in 1996, there have been marked and
sustained reductions in AIDS-related mortality, opportunistic infections, and AIDSrelated malignancies.66-71 Today, an individual diagnosed with HIV at the age of 20 is
expected to survive past the age of 70.72,73 As HIV infection itself has become a more
chronic and manageable condition, other chronic diseases are emerging as increasingly
important contributors to morbidity and mortality among persons living with HIV
(PLWH). For example, before ART become widely available, non-AIDS-defining
cancers accounted for less than 1% of deaths among PLWH, but more recent studies
suggest that 8-27% of deaths in the modern era are attributable to such cancers.71,74-77
Cardiovascular disease is now responsible for about a quarter of all deaths among PLWH
in the United States.67,69 Liver failure has also emerged as a major cause of death in
PLWH.78-80
Because of shared routes of transmission, co-infection with hepatitis B virus
(HBV) and hepatitis C virus (HCV) is common among PLWH, contributing significantly
to liver-related morbidity and mortality in this population.81-83 Even with virologic
suppression via ART, PLWH continue to have evidence of immune deficiency,
dysregulation, and activation that may contribute to increased risk and severity of nonAIDS comorbidities, including the liver disease associated with viral hepatitis.84-88 HBV
and HCV may each lead to cirrhosis and hepatocellular carcinoma and, in both cases, coinfection with HIV accelerates progression to these end-stage manifestations of
disease.89-94
6
Worldwide, HBV is the leading cause of chronic liver disease and is estimated to
be responsible for 620,000 deaths per year.95,96 In the United States and Western Europe,
6-14% of PLWH are also chronically infected with HBV.81 The risk of liver-related
mortality increases eight-fold in the setting of HIV/HBV co-infection compared to HIV
mono-infection and all-cause mortality is increased beyond the mortality rate of either
infection alone.97 Drugs with activity against both HIV and HBV—such as tenofovir,
lamivudine and emtricitabine—are effective at reducing HBV viral load, but patients
treated with these drugs remain at risk of developing end-stage liver disease and its
complications.98
In the United States, Europe and Australia, approximately 20-30% of PLWH are
also chronically infected with HCV.81-83 Although spontaneous recovery from HCV
infection occurs in 20%-30% of people without HIV, clearance occurs in only 5-15% of
people co-infected with HIV.99-102 Clinical manifestations of liver failure secondary to
HCV such as ascites, encephalopathy and esophageal varices are all more common in the
presence of HIV co-infection and contribute to increased morbidity among HIV/HCV coinfected patients as compared to HIV mono-infected patients.103-105 HCV is also
associated with extrahepatic complications such as renal disease, cardiovascular disease,
diabetes, and neuropsychiatric disorders.106-110
Because it is such a common comorbidity among PLWH, viral hepatitis is quickly
emerging as one of the most important contributors to morbidity and mortality in this
population. As the HIV-infected population in the United States continues to age,
diseases that are traditionally associated with HIV, like pneumocystic pneumonia and
Kaposi sarcoma, are likely to play a diminishing role in morbidity and mortality among
7
PLWH. Chronic illnesses such as liver disease, renal disease, and cardiovascular disease
will play an increasingly important role in influencing overall health and healthcare needs
among PLWH.
FAILURES OF HEALTHCARE IN THE UNITED STATES FOR PERSONS
LIVING WITH HIV
Presently in the United States, most healthcare transactions involve three parties:
the billing healthcare provider; the patient, who pays a portion of the costs of healthcare
directly to the provider; and a third-part payor, which negotiates payment rates with
healthcare providers and fills the gap between those negotiated costs and individual outof-pocket contributions. These third-party payors may be private insurance companies or
the publicly-funded programs, Medicare or Medicaid. Among the publicly-funded
programs, Medicare primarily provides coverage to seniors and persons with qualifying
disabilities and Medicaid provides coverage to persons with low income. Some people
are eligible for both programs.
In 2009, 81.1% of PLWH in a nationally representative sample had health
insurance, including 40.3% with Medicaid, 30.6% with private health insurance, and
25.7% with Medicare.111 Furthermore, among PLWH diagnosed from May 2004-April
2009, 37% reported a lapse in insurance coverage during the previous 12 months and this
lapse was independently associated with delays in initiation of life-saving ART as
compared to persons with continuous coverage.112 The distribution of third-party payors
among PLWH is very different from that observed in the general U.S. population, in
which 63.9% of persons were privately insured, 15.7% had Medicaid, and 14.1% had
8
Medicare in 2009.113 These differences are driven largely by the fact that HIV
disproportionately affects individuals with lower socioeconomic status. In 2009, 43.8%
of PLWH had household incomes at or below federal poverty guidelines.111
Health insurance coverage is highly correlated with overall health. People with
health insurance are more likely than those who are uninsured to use preventive services
and enjoy better clinical outcomes, including lower risk of mortality.114-117 Among
PLWH, health insurance coverage is associated with a greater likelihood of sustained
virologic suppression, lower use of acute care services, and decreased risk of developing
clinical AIDS.118-120
Even among PLWH who are insured, underinsurance may remain an issue. For
example, in order to receive prescription drug coverage through Medicare, individuals
must opt to enroll into the Medicare Part D program, which requires payment of a
separate premium. These plans have complex schedules of cost-sharing tiers, deductibles
and co-payments that may each leave patients with significant healthcare costs. Most
plans also have coverage gaps, commonly referred to as “doughnut holes,” wherein the
insurer covers a portion of medication costs up to a certain annual allowance. After
reaching this allowance, individuals are responsible for the full cost of prescription drugs
until Medicare catastrophic prescription coverage kicks in and again starts covering such
expenses. The existence of “doughnut holes” in Medicare prescription drug plans can
cost insured PLWH thousands of dollars, creating a precarious situation for low-income
patients who may not always be able to afford medications and are living with a disease
that requires strict medication adherence.121
9
Since passage of the Ryan White Comprehensive AIDS Resources Emergency
(CARE) Act in 1990, federal and state governments have provided financial support to
deliver HIV care, medications and social services that were not otherwise available to
uninsured and underinsured low-income PLWH. In 2012, $2.4 billion was allocated from
the federal budget to support the Ryan White HIV/AIDS Program.122 This included over
$1 billion allocated to AIDS Drug Assistance Programs (ADAPs) that are administered
separately by each of the 50 states, the District of Columbia, the Commonwealth of
Puerto Rico, the U.S. Virgin Islands, American Samoa, the Federated States of
Micronesia, Guam, the Northern Mariana Islands, Republic of Palau and the Republic of
the Marshall Islands. These programs provide medications—principally antiretrovirals—
to low income, uninsured, and underinsured PLWH who would otherwise not have access
to these life-saving medications. Additional state contributions and drug rebates brought
the total budget for these programs to over $2 billion in 2012 and 2013.123 ADAPs are
designed to be a “payer of last resort,” to be relied upon when all other avenues for
acquiring medications are exhausted. Despite significant spending, ADAP budgets still
often fell short of demand. From 2008 to 2013, it was not uncommon for state programs
to have waiting lists, forcing ADAPs to limit formularies and ration drugs.122,124 It was
not until November 11, 2013, that ADAP waiting lists were finally cleared and all
eligible patients enrolled, largely owing to an influx of emergency funding from the
federal government that began in 2010.123
Even with all eligible patients enrolled in ADAPs, coverage gaps exist. Individual
states determine whether to contribute to ADAPs, how much to contribute, what drugs to
include on formularies, and what patient characteristics determine eligibility for these
10
programs. In 2013, 16 of 51 ADAPs reporting data did not receive any state
contributions.123 In 2010, only Massachusetts, New Jersey, New York and Pennsylvania
included guideline-based medications for diabetes, hypertension, dyslipidemia, and
smoking cessation on their ADAP formularies; thirteen states listed no drugs for any of
these chronic comorbidities on their formularies.125 This is despite the fact that treatment
for each of these could reduce the prevalence of cardiovascular disease, which is
disproportionately common among PLWH as compared to the general population and
accounts for about a quarter of all deaths.67,69,126
The U.S. government spends a considerable amount of money on specialized
programs for the support of HIV-related healthcare. These programs are unlike those
afforded to any other disease. Still, the system falls short of meeting the increasing
healthcare needs of a growing, aging, and largely underinsured HIV-infected population.
CHANGES IN HEALTHCARE IN THE UNITED STATES FOR PERSONS
LIVING WITH HIV
The Patient Protection and Affordable Care Act (ACA) is poised to dramatically
alter the way healthcare is delivered in the United States, with particular impact among
PLWH. Changes have already begun and will gradually roll out through 2019. Among
the most important changes for PLWH is the expansion of Medicaid coverage in some
states to include all people with incomes below 138% of the federal poverty level.122
Eligibility requirements vary by state and, in 2012, the median income specified by states
as the maximum allowable in order to qualify for Medicaid was only 61% of the federal
poverty level for adults with children; only nine states offered full coverage to adults
11
without children.127 Individuals also needed to meet additional requirements such as
having a disabling condition or being pregnant. An AIDS diagnosis is considered a
disabling condition and therefore is the mechanism through which many low-income
PLWH have qualified for Medicaid, despite clear evidence that initiation of ART before
progressing to AIDS reduces disease complications and prolongs survival.64,65,128 Among
PLWH currently served by state ADAPs, 53% meet the new financial criterion for
Medicaid enrollment, potentially heralding a huge shift away from dependence on
ADAPs as more states expand their Medicaid programs under the ACA.123
The ACA also mandates that dependents up to the age of 26 may be included on
parents’ insurance plans. Before the law, dependent children often lost this option for
coverage when they turned 19, or 22 if they were full-time students. Given that over 17%
of new HIV diagnoses occur among individuals aged 19-25, this is a potentially
important new source of coverage for this group.129
As of 2014, uninsured PLWH and other individuals whose incomes are too high
to qualify for Medicaid may seek coverage through state health insurance exchanges with
subsidies provided on a sliding scale to households earning 100-400% of the federal
poverty level. Insurance plans on these exchanges must conform to specific regulations.
Among the important new regulations is a ban on insurance companies refusing to
provide coverage due to pre-existing conditions, such as HIV infection. Insurance plans
on the exchanges may also not impose annual or lifetime caps on coverage, which is
important for PLWH whose average lifetime costs of care may exceed $400,000 when
diagnosed late in the course of disease.130 Finally, insurance plans must include coverage
for certain essential benefits. These include inpatient, outpatient and emergency
12
healthcare services; mental health and substance abuse treatment, including behavioral
counseling services; laboratory services; and prescription drug coverage. Coverage of
expensive ART regimens by insurers on health exchanges may help relieve some of the
burden on overstretched ADAPs. Among PLWH, rates of mental illness and substance
abuse are high, making coverage for services to treat these disorders especially
important.131 Treatment for these disorders may also improve adherence to ART and slow
the clinical progression of HIV.132-134
Implementation of the ACA will also have important implications for the
diagnosis of new HIV infections. Presently, it is estimated that about 20% of PLWH in
the United States are not aware of their diagnosis and this group is responsible for about
half of HIV transmission.135 For multiple reasons, expanding insurance coverage is likely
to result in increased screening for HIV among all Americans. First, insurance lowers the
cost of HIV testing to individual patients. Second, insurance increases opportunities for
testing by engaging individuals with the healthcare system. Third, insurance provides
resources to facilitate treatment of the disease, if detected, thereby making screening
more valuable than it would otherwise be if treatment were unavailable. Because of these
factors, it is estimated that Medicaid expansion alone will result in the screening of
450,000-600,000 additional individuals and the diagnosis of 2,500-3,300 additional HIV
infections by 2017.136 Increases in routine HIV testing will enable earlier initiation of
treatment that prolongs survival and reduces disease transmission.130,137-139
COSTS OF HEALTHCARE AMONG PERSONS LIVING WITH HIV
13
The expansion of publicly funded insurance programs and subsequent increased
engagement of PLWH in treatment and care is not expected to come cheaply. In 1995,
the federal government allocated $3.7 billion to HIV care, in 2004 this number had nearly
tripled to $11.0 billion, and in 2015 the government is expected to spend $17.5 billion on
health care services and treatment for PLWH in the United States.140,141 By improving
survival, early diagnosis and treatment of HIV infection actually increases lifetime costs
of care for PLWH.130 Medications, laboratory services, outpatient healthcare utilization,
and inpatient healthcare utilization each contribute to healthcare costs. Understanding the
sources of healthcare spending and the factors that influence healthcare utilization among
PLWH is important to policy-makers and third-party payors who are tasked with
allocating a limited pool of healthcare resources.
Medications are, by far, the largest component of healthcare costs among PLWH.
Even accounting for discounted pricing negotiated between ADAPs and pharmaceutical
companies, typical ART regimens can cost $14,000-$30,000 per patient, per
year.130,142,143 On average, non-HIV-related medications contribute an additional $2,400
per year. When a patient’s CD4 count is low, medications for prophylaxis against
opportunistic infections add another $1,000 per year. Among PLWH with well-controlled
disease, medications are the source of 66-78% of healthcare spending. As CD4 counts
decline, medication costs tend to rise slightly, but the increase in medication costs is
outpaced by other sources of healthcare spending, such as inpatient healthcare utilization.
It is only when CD4 count dips below 50 cells/mm3 that medications no longer represent
the majority of healthcare costs.142,143 Medications are estimated to cost $351,000 over
14
the lifetime of care for someone diagnosed late in his or her disease course and $581,000
for someone diagnosed early.130 As life expectancies for PLWH continue to rise, the costs
of lifelong ART can also be expected to rise. When patents on the earliest generations of
antiretroviral drugs expire, cheaper generic versions of these drugs will become available
in the United States. Healthcare providers and policy-makers will face difficult decisions
regarding how to balance the cost-savings of generic medications with the often better
tolerability, efficacy, and ease-of-use associated with newer, brand-name medications.144
Laboratory costs represent the smallest component of healthcare costs among
PLWH. Baseline tests include HIV drug resistance genotyping and screening for various
antibodies and comorbidities. Also, HIV RNA, CD4 count, blood counts, kidney and
liver function testing are performed at baseline and then again at regular intervals.64,65 As
PLWH live longer, the number of tests performed over their lifetimes may increase, but
the costs of these tests are also declining substantially as laboratory technology improves.
In 1997, an HIV RNA test cost between $125 and $198.145 In 2008, the test cost $90.142
Guidelines are also moving toward less frequent laboratory monitoring among PLWH
with well-controlled disease.64,65 Presently, laboratory costs account for less than 5% of
healthcare spending among PLWH.142,143
Despite routine outpatient primary HIV care visits typically scheduled every 3-6
months even for persons with well-controlled HIV, outpatient care represents a relatively
small portion of healthcare costs. According to the Medicare National Physician Fee
Schedule for 2014, the national facility unit cost for an outpatient visit billing for the
most complex level of patient evaluation and management (CPT code 99215) was only
15
$111.41.146 Outpatient visits contribute only 2-10% toward the overall costs of care
among PLWH, varying slightly according to CD4 count.142,143
Hospitalization is a particularly costly form of healthcare utilization and is
potentially modifiable, therefore warranting special attention. Among persons with a CD4
count above 500 cells/mm3, hospitalizations account for just 10-14% of healthcare costs.
Among persons with a CD4 count below 50 cells/mm3, they account for 49-60% of
costs.142,143 This underscores the importance of hospitalization as a marker of disease
severity. It also highlights the importance of early initiation of ART in order to prevent
CD4 decline, halt disease progression, and reduce inpatient utilization. Interventions such
as influenza vaccination and pneumococcal vaccination have been shown to reduce
hospitalization rates and healthcare costs in specific populations and are now
recommended for all PLWH in the United States.64,147-149 Identifying additional
interventions to reduce hospitalizations among PLWH should be a research priority and
requires understanding of the factors that contribute to hospitalization risk in this
population.
ELITE CONTROL AND THE PROMISE OF HIV REMISSION
Within 30 years of its discovery, HIV transitioned from a near-certain death
sentence to a chronic and manageable condition. As of 2014, four different medication
regimens are available that can control the disease via a single daily pill.63 Reductions in
AIDS-related mortality and complications of HIV have been mirrored by decreased
hospitalization rates among PLWH.66-71,119,150-153 Life expectancy among persons newly
diagnosed with HIV is approaching that of the general U.S. population.72,73 Stem cell
16
transplantation using genetically mutated donor cells has allowed one HIV-infected
patient to live without evidence of active infection for over 7 years without ART154 and
extremely early initiation of ART has produced a handful of patients capable of
controlling the virus without specific therapy for prolonged periods of time.155-157 In the
setting of all these advances, there is unprecedented optimism that a functional cure of
HIV may one day be possible. Functional cure may potentially come from measures to
eradicate all replication-competent virus from an individual (“sterilizing cure”) or from
measures to induce durable, host-mediated control of the virus without ART (“HIV
remission”).
Elite controllers are a unique subgroup of PLWH that may inform efforts to
achieve HIV remission. They represent fewer than 1% of all PLWH and are characterized
by their natural ability to suppress HIV in the peripheral blood to levels below the limit of
detection via conventional assays without any specific therapy.158 This appears to be
achieved primarily via potent HIV-specific host responses.159-162 It does not appear that
elite controllers are infected with virus that is any less virulent than that infecting other
PLWH.163,164 In most cases, HIV RNA and DNA are detectable among elite controllers at
very low levels in various body fluids and tissues.165-168 Nonetheless, elite controllers
generally experience little or no disease progression for prolonged periods of time.158 For
this reason, they have been cited as a possible model for the functional cure of HIV and
experts have considered whether induction of a state similar to elite control should be
considered a goal when pursuing an HIV cure.169
There are, however, disadvantages inherent to elite control that may make it less
desirable than a sterilizing cure or other methods of controlling the virus, such as ART. In
17
many cases, despite being undetectable via conventional assays, viral replication persists
among elite controllers at levels that are actually higher than those seen among persons
who are medically controlled with ART.167,168 Elite controllers have evidence of
microbial translocation from the gut, T cell activation, and inflammation that is higher
than that seen among HIV-uninfected persons or PLWH who are well-controlled on ART.
170-172
These high levels of inflammation may also drive the observation that elite
controllers have more coronary atherosclerosis than do HIV-uninfected patients, even
after adjusting for traditional cardiovascular risk factors.173,174 Despite virologic control,
some elite controllers still experience substantial CD4 decline and progress to clinical
AIDS.170 Data on clinical outcomes among elite controllers are scarce, but one study has
demonstrated similar rates of non-AIDS events in both elite controllers and untreated
non-controllers.175 Giving ART to elite controllers can increase CD4 count, decrease HIV
RNA levels in the plasma and tissues, and decrease markers of T cell activation and
inflammation.176 More data is needed on clinical outcomes among elite controllers, which
is difficult to obtain because of their relative rarity among PLWH, but the existing data
suggest that medical control of HIV with ART may be superior to the natural control of
disease that is demonstrated by elite controllers. Therefore, elite control may not be an
optimal model for HIV remission.
SPECIFIC AIMS
HIV infection has become a chronic condition that requires expensive healthcare.
As PLWH live longer and a greater proportion are connected with healthcare resources in
the United States, total healthcare utilization by this population can only be expected to
18
increase. With national methods for funding and delivering healthcare in flux, it is critical
to understand the factors associated with healthcare utilization among PLWH. Factors
associated with hospitalizations are of particular importance, because this is a costly form
of healthcare and also a marker of morbidity. As morbidity and mortality directly
attributable to HIV have declined, viral hepatitis has emerged as an especially important
comorbidity and potentially a driver of healthcare costs among PLWH. Clarifying the
role of viral hepatitis as a driver of healthcare costs among PLWH is critical as new,
more tolerable, and more effective therapies could transform this diagnosis into a curable
disease and, therefore, a modifiable risk factor for healthcare utilization among
PLWH.177-179 Elite controllers have emerged as an important subgroup of PLWH as
scientific attention has turned toward efforts to achieve a functional cure of HIV.
Investigating hospitalizations among this population provides data to inform the clinical
care of elite controllers and may help clarify the value of pursuing therapeutic efforts that
might seek to induce an elite control-like state.
In the chapters that follow, healthcare utilization among PLWH will be explored
with particular emphasis on the roles of hepatitis co-infection and elite control. This
research uses data collected by the HIV Research Network (HIVRN), a consortium of
specialty HIV care clinics in 11 cities across the United States. The HIVRN has been
collecting de-identified demographic, clinical, and health services utilization data since
2000. Participating clinics are mostly in urban settings and include entities with and
without academic affiliations. These clinics provide care to more than 20,000 PLWH
every year.
19
Chapter 2 is entitled, “Impact of Hepatitis Co-Infection on Hospitalization Rates
and Causes in a Multi-Center Cohort of Persons Living with HIV.” This work has been
published in JAIDS: Journal of Acquired Immune Deficiency Syndromes and is reprinted
with permission. The specific aim of this study was to characterize the impact of hepatitis
co-infection on hospitalizations among PLWH, exploring both overall hospitalization
rates and reasons for hospitalization. By evaluating 2,793 hospitalizations occurring
during a single year, this study clarifies the roles of hepatitis B and hepatitis C in
contributing to morbidity and healthcare costs among PLWH. Investigation of specific
reasons for admission shed light on potential pathogenic mechanisms and opportunities
for risk reduction.
Chapter 3 is entitled, “Impact of Hepatitis Co-Infection on Healthcare Utilization
among Persons Living with HIV.” This work has been published in JAIDS: Journal of
Acquired Immune Deficiency Syndromes and is reprinted with permission. The specific
aim of this study was to characterize the impact of hepatitis co-infection on utilization of
primary HIV care, mental health, and inpatient services from 2006-2011. This study
clarifies the roles of hepatitis B and hepatitis C in contributing to various sources of
healthcare costs among PLWH in the United States. By evaluating trends over time,
inferences may be drawn about future utilization patterns and costs as well.
Chapter 4 is entitled, “Elite Controllers are Hospitalized More Often than Persons
with Medically Controlled HIV.” This work has been published in The Journal of
Infectious Diseases and is reprinted with permission. By investigating rates and reasons
for hospitalization among 149 elite controllers as compared to persons who were wellcontrolled on ART from 2005-2011, this study provides rare clinical data on differences
20
between elite and medical control of HIV infection. These data may inform the clinical
care of elite controllers, in whom there has long been a question of whether ART may be
beneficial. They also add a clinical component to the findings of prior evaluations that
showed differences in laboratory markers and imaging that could portend worse clinical
outcomes among elite controllers as compared to persons who are well-controlled with
ART.
21
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34
CHAPTER 2:
IMPACT OF HEPATITIS CO-INFECTION ON HOSPITALIZATION
RATES AND CAUSES IN A MULTI-CENTER COHORT OF
PERSONS LIVING WITH HIV
Reprinted from:
Crowell TA, Gebo KA, Balagopal A, Fleishman JA, Agwu AL, Berry SA, for the HIV
Research Network. “Impact of Hepatitis Coinfection on Hospitalization Rates and Causes
in a Multicenter Cohort of Persons Living with HIV.” JAIDS: Journal of Acquired
Immune Deficiency Syndromes. 2014 Apr 1;65(4):429-37.
By permission of Wolters Kluwer Health, Lippincott Williams & Wilkins©
35
ABSTRACT
Background: Chronic viral hepatitis is a potentially important determinant of healthcare
utilization among persons living with HIV (PLWH). We describe hospitalization rates
and reasons for hospitalization among PLWH stratified by co-infection with hepatitis B
virus (HBV) and/or hepatitis C virus (HCV).
Methods: Laboratory, demographic, and hospitalization data were obtained for all
patients receiving longitudinal HIV care during 2010 at 9 geographically diverse sites.
Hepatitis serostatus was assessed by hepatitis B surface antigen and/or hepatitis C
antibody. ICD-9 codes were used to assign hospitalizations into diagnostic categories.
Negative binomial regression was used to assess factors associated with all-cause and
diagnostic category-specific hospitalizations.
Results: A total of 2,793 hospitalizations were observed among 12,819 patients. Of these
patients, 49.3% had HIV mono-infection, 4.1% HIV/HBV, 15.4% HIV/HCV, 2.5%
HIV/HBV/HCV and 28.7% unknown hepatitis serostatus. Compared to HIV monoinfection, risk of all-cause hospitalization was increased with HIV/HBV (adjusted
incidence rate ratio (aIRR) 1.55 [1.17-2.06]), HIV/HCV (1.45 [1.21-1.74]) and
HIV/HBV/HCV (1.52 [1.04-2.22]). Risk of hospitalization for non-AIDS-defining
infection was also higher among patients with HIV/HBV (2.07 [1.38-3.11]), HIV/HCV
(1.81 [1.36-2.40]) and HIV/HBV/HCV (1.96 [1.11-3.46]). HIV/HBV was associated with
hospitalization for gastrointestinal/liver disease (2.55 [1.30-5.01]). HIV/HCV was
associated with hospitalization for psychiatric illness (1.89 [1.11-3.26]).
Conclusions: HBV and HCV co-infection are associated with increased risk of all-cause
hospitalization and hospitalization for non-AIDS-defining infections, as compared to HIV
36
mono-infection. Policy-makers and third-party payers should be aware of the heightened
risk of hospitalization associated with co-infection when allocating healthcare resources
and considering models of healthcare delivery.
37
BACKGROUND
Chronic viral hepatitis is common among persons living with HIV (PLWH). In
the United States, Europe, and Australia, approximately 4.8-9.0% of PLWH are also
chronically infected with hepatitis B virus (HBV), 20-33% are chronically infected with
hepatitis C virus (HCV) and 0.5-4.0% are chronically infected with both1-5. Patients with
HIV/HBV co-infection experience faster progression to cirrhosis, more hepatocellular
carcinoma and higher risk of liver-related mortality than patients with either infection
alone5-8. Similarly, liver disease progression and its complications are more common in
HIV/HCV co-infected patients than in HIV mono-infected patients2,8,9. Viral hepatitis,
particularly HCV, has also been associated with extrahepatic complications that can
include renal disease, cardiovascular disease, diabetes, autoimmunity, metabolic bone
disease and neurocognitive decline10-16.
In the era of potent and widely available antiretroviral therapy, hospitalization
rates have become an important outcome measure and an important healthcare cost
among PLWH17-19. Comparing rates and reasons for hospitalizations among PLWH with
and without hepatitis co-infection will be important to clinicians and policy-makers
trying to understand the healthcare needs of these populations. Differences across these
populations could suggest areas of unique clinical need and may influence the allocation
of healthcare resources and the construction of healthcare delivery models.
The purpose of this study was to characterize the impact of hepatitis co-infection on
inpatient healthcare utilization among HIV-infected patients in a multi-site, multi-state
consortium of HIV care sites.
38
METHODS
Site Selection and Data Collection
The HIV Research Network (HIVRN) is a consortium of 17 sites providing
longitudinal adult and pediatric HIV care in 11 U.S. cities. Sites abstract comprehensive
demographic, laboratory, and treatment data from clinical records, then de-identify and
submit them to a data coordinating center where they are reviewed and combined into a
uniform database20. In 2010, nine of the participating sites submitted details of hospital
admissions for adult patients (3 Northeast, 3 West, 2 South, and 1 Midwest). Seven of
these sites have academic affiliations and 2 are community-based. Inclusion in this
retrospective cohort study was restricted to patients who enrolled in care before July 1,
2010, and were in active care during 2010. Active care was defined as having at least one
outpatient HIV provider visit and one CD4 cell count during the calendar year.
Institutional review boards at each site and at the data coordinating center at Johns
Hopkins University approved the collection and use of these data for analysis and
publication.
Definitions of Variables
Hepatitis serostatus was assessed by detection of hepatitis B surface antigen
(HBsAg) and/or hepatitis C antibody (anti-HCV) at any time prior to or during 2010. At
each site, patients are screened for chronic HBV and chronic HCV at the discretion of
their providers. If a patient had multiple serologies performed over time, a single positive
test was considered sufficient to categorize the patient as positive for that assay. HBV
DNA and HCV RNA levels were not available. Patients were assigned to one of five
39
hepatitis serostatus categories. Patients with negative results for both hepatitis serologies
were categorized as HIV mono-infected. Patients with a positive HBsAg and negative
anti-HCV were categorized as HIV/HBV co-infected. Patients with a negative HBsAg
and positive anti-HCV were categorized as HIV/HCV co-infected. Patients with positive
results for both hepatitis serologies were categorized as HIV/HBV/HCV tri-infected.
Patients without known results from one or both tests were categorized as unknown
hepatitis serostatus.
Age was assessed on July 1, 2010 and divided into 4 categories: 18-34, 35-49, 5064 and 65 or more years. Race/ethnicity was categorized based on self-report as White,
Black, Hispanic or other/unknown. HIV transmission risk factor was classified as one of
four mutually exclusive categories: injection drug use (IDU), men who have sex with
men (MSM), heterosexual transmission, or other/unknown. Patients who reported IDU in
addition to any other risk factor were categorized as IDU. Men who reported sex with
both men and women were categorized as MSM.
The CD4 T-cell count and HIV-1 RNA values used in this analysis were the first
available measurements in 2010. CD4 count was categorized as ≤50, 51-200, 201-500 or
>500 cells/mm3. HIV-1 RNA was categorized as <400 or ≥400 copies/mL. Antiretroviral
therapy (ART) was defined as the concurrent use of three or more antiretroviral
medications from at least two classes at any time during calendar year 2010. Insurance
status was categorized as Medicaid, Medicare, Private, Ryan White/Uninsured or missing.
Patients with dual eligibility for Medicaid and Medicare were included in the Medicare
category.
40
Outcomes
The primary outcome of this study was all-cause hospitalization in 2010. We also
investigated cause-specific hospitalization rates using 18 diagnostic categories, including
non-AIDS-defining infection, cardiovascular, gastrointestinal/liver and AIDS-defining
illness (see Table, Supplemental Digital Content 1, for complete list). Using a previously
published algorithm, several steps were taken to assign each hospitalization to a single
diagnostic category18,21. First, the primary diagnostic code for the hospitalization was
assigned using the first-listed ICD-9 code that did not refer to HIV (042, V08, 795.71,
V01.79), chronic HBV (070.22, 070.23, 070.32, 070.33), chronic HCV (070.44, 070.54,
070.70, 070.71), or oral candidiasis (112.0). These codes represent comorbidities that are
frequently recorded for billing purposes but are not, by themselves, sufficient to justify
hospitalization. Second, Clinical Classifications Software (CCS) developed by the
Agency for Healthcare Research and Quality was used to assign the primary ICD-9 code
into one of 18 “first-level” CCS categories 22. Finally, we modified the CCS diagnostic
categories in three ways: we reassigned infections from organ system categories to the
infection category (for example pneumonia was reassigned from pulmonary to infection);
we combined congenital, perinatal, and unclassified (together < 1% of admissions) into a
single category; and we created an AIDS-defining illness (ADI) category according to the
1993 Centers for Disease Control and Prevention criteria 23. After the ADI category was
created, we renamed the remaining infection category “non-AIDS-defining infection” and
the remaining malignancy category “non-AIDS-defining cancer.”
Within each diagnostic category, ICD-9 codes were used to identify the most
frequently occurring individual diagnoses. Highly similar ICD-9 codes were grouped
41
(Appendix Table). The most common individual diagnoses were tallied and reported as
percentages of admissions within the corresponding diagnostic category.
Data Analysis
Hospitalization rates were calculated as total number of admissions divided by the
number of years of patient follow-up and multiplied by 100 to obtain rates per 100
person-years (PY). Patients who enrolled in care or died during the observation period
contributed less than one year of follow-up, so a variable person-time denominator was
used in rate calculations.
Preliminary exploration of the hospitalization count data revealed that the
variance was not equal to the mean of the distribution, making negative binomial
regression a more robust analytic method than Poisson regression. Unadjusted negative
binomial regression was therefore used to estimate incidence rate ratios for all-cause and
diagnostic category-specific hospitalization rates associated with hepatitis serostatus and
other predefined clinical and demographic variables.
Adjusted negative binomial models compared incidence rates for all-cause
hospitalization and diagnostic category-specific hospitalizations between each of the
hepatitis serostatus groups (including the unknown group), controlling for age, race, sex,
HIV risk factor, CD4, HIV RNA, ART use, and insurance. Adjusted models also
included categorical indicators for each clinical care site to control for site-specific
variability in healthcare utilization (results suppressed).
42
A sensitivity analysis was performed in which patients with one positive hepatitis
serology and one missing hepatitis serology were re-categorized from the unknown
hepatitis status group into either the HIV/HBV or HIV/HCV group.
A two-sided type I error of 5% was considered statistically significant. All analyses were
performed using Stata 12.0 (StataCorp LP, College Station, TX, USA).
RESULTS
Demographic and clinical characteristics of the study population are presented in
Table 2-1. Of the 12,819 patients included in this analysis, 49.3% had HIV monoinfection, 4.1% HIV/HBV co-infection, 15.4% HIV/HCV co-infection, 2.5%
HIV/HBV/HCV tri-infection and 28.7% had unknown hepatitis serostatus. IDU was
reported in 17.4% of patients overall with higher percentages in the HIV/HCV (59.4%)
and HIV/HBV/HCV (32.7%) groups. MSM comprised 39.3% of patients overall with
higher percentages in the HIV/HBV (56.6%) and HIV mono-infected (47.4%) groups.
MSM was relatively less common as a sole HIV risk factor in the HIV/HCV (15.7%) and
HIV/HBV/HCV (26.1%) groups. Median CD4 counts and percentages of patients with
HIV RNA <400 copies/mL were similar across all the hepatitis serostatus groups. There
were 117 deaths and 885 new enrollments in care during the study period, resulting in
less than one year of observation time for these individuals. Median follow-up of these
patients was 230 days among patients in the HIV/HBV group and 245 days in all other
hepatitis serostatus groups.
There were 2,793 hospitalizations in total. Unadjusted all-cause hospitalization
rates stratified by hepatitis serostatus are presented in Figure 2-1 (panel A). Rates were
43
highest among HIV/HCV co-infected patients (41.1 hospitalizations per 100 PY [95% CI
35.7-47.2]), followed by HIV/HBV co-infected (35.4/100 PY [26.6-47.0]), then
HIV/HBV/HCV tri-infected (28.2/100 PY [19.4-40.9]). All-cause hospitalization rates
were similar among HIV mono-infected patients (19.5/100 PY [17.9-21.3]) and patients
with unknown hepatitis serostatus (18.2/100 PY [16.2-20.5]).
Analyses of factors associated with all-cause hospitalization are presented in
Table 2-2. Decreasing CD4 count was the strongest predictor of all-cause hospitalization
with an adjusted incidence rate ratio (aIRR) of 8.14 (95% CI 6.27-10.58) for persons with
CD4 <50 cells/mm3, compared to CD4 >500 cells/mm3. Risk of hospitalization increased
in those with HIV/HBV (aIRR 1.55 [1.17-2.06]), HIV/HCV (aIRR 1.45 [1.21-1.74]) and
HIV/HBV/HCV (aIRR 1.52 [1.04-2.22]) compared to HIV mono-infection. Other factors
independently associated with hospitalization included age, gender, HIV transmission
risk factor, HIV-1 RNA, and insurance.
In unadjusted analyses, non-AIDS-defining infections accounted for significantly
more hospitalizations per 100 person-years in each of the hepatitis co-infected groups
than in the HIV mono-infected group (Figure 2-1, panel B). Gastrointestinal/liver-related
hospitalizations were more common in the HIV/HBV (5.6 per 100 PY [2.9-10.7]) and
HIV/HCV (4.0 per 100 PY [2.9-5.6]) groups than in the HIV mono-infected group (1.6
per 100 PY [1.3-2.0]). Compared to HIV mono-infected patients, patients with HIV/HCV
had significantly higher unadjusted hospitalization rates in the cardiovascular, renal,
psychiatric, pulmonary, and injury/poisoning categories (p<0.001).
Adjusted relative rates of hospitalization for the ten most common diagnostic
categories are presented in Figure 2-2. Compared to HIV mono-infection, the relative rate
44
of hospitalization for non-AIDS-defining infection was higher among patients with
HIV/HBV (aIRR 2.07 [1.38-3.11]), HIV/HCV (aIRR 1.81 [1.36-2.40]) and
HIV/HBV/HCV (aIRR 1.96 [1.11-3.46]). The relationship between hepatitis co-infection
and hospitalization for gastrointestinal/liver disease was attenuated in multivariate
analysis, with only HIV/HBV remaining independently associated with risk of
hospitalization (aIRR 2.55 [1.30-5.01]). Patients with HIV/HCV had higher risk of
hospitalization for psychiatric illness (aIRR 1.89 [1.11-3.26]) and patients with
HIV/HBV had higher risk of hospitalization for non-AIDS-defining cancers (aIRR 4.75
[1.52-14.88]) than the HIV mono-infected reference group.
Table 2-3 lists the most common diagnostic categories and individual diagnoses
within these categories. Among non-AIDS-defining infections, bacterial pneumonia was
the most common diagnosis overall and among most hepatitis serostatus groups.
Complications of cirrhosis (including admissions for cirrhosis, hepatic encephalopathy,
portal hypertension and ascites) were the most common reason for GI/liver admissions
overall, although the proportion of admissions did not differ significantly from the
proportions for pancreatitis or diarrhea. Complications of cirrhosis accounted for only
4.12% of GI/liver admissions in the HIV mono-infected group. Among AIDS-defining
illnesses, Pneumocystis jiroveci was the most common diagnosis overall and among most
hepatitis serostatus groups.
In our sensitivity analysis, 67 participants from the unknown hepatitis serostatus
group were recategorized as HIV/HBV co-infected and 291 as HIV/HCV co-infected on
the basis of one positive serology and an unknown second hepatitis serology.
Multivariable models using this definition of hepatitis serostatus yielded similar results to
45
our original analysis. Compared to HIV mono-infection, the relative rate of all-cause
hospitalization was again increased in those with HIV/HBV (IRR 1.50 [1.15-1.97]),
HIV/HCV (IRR 1.38 [1.16-1.65]) and HIV/HBV/HCV (IRR 1.52 [1.04-2.22]). Inferences
about the relationship between hepatitis serostatus and risk of diagnostic categoryspecific hospitalizations were also unchanged (data not shown).
DISCUSSION
This study is the first to demonstrate, in a contemporary cohort of PLWH, that
hospitalization rates are higher among patients with HBV and/or HCV co-infection. This
finding is consistent with prior studies demonstrating high rates of morbidity and
mortality in co-infected populations2,5-13,16. Since hospitalizations are a significant driver
of healthcare costs among PLWH, the higher frequency of hospitalization in hepatitis coinfected populations results in increased healthcare costs for these populations19. Policymakers should be aware of the financial implications of co-infection among PLWH as
they allocate scarce healthcare resources and establish capitated costs for accountable
care organizations.
Non-AIDS-defining infections accounted for about a quarter of all hospital
admissions, and the relative risk of hospitalization for this reason was elevated among
patients in all the hepatitis-infected categories. Consistent with prior studies, the most
common non-AIDS-defining infection in our study was bacterial pneumonia24-26. Chronic
viral hepatitis is known to be associated with dysregulation of hepatitis-specific immune
responses, but further investigation is needed to explore potential mechanisms underlying
an increased risk of bacterial infections27,28. Preventable infections such as influenza and
46
pneumococcal pneumonia should be proactively addressed in PLWH with appropriate
vaccinations to potentially reduce morbidity and hospitalizations29,30.
While complications of cirrhosis among PLWH with viral hepatitis co-infection
deserve attention due to their seriousness and their associations with mortality, such
hospitalizations accounted for only 2.8% (28 out of 999, Table 2-3) of all hospitalizations
among the three viral hepatitis groups combined5-9. We did not perform formal diagnostic
tests specifically on complications of cirrhosis because of small sample sizes. By way of
comparison, however, they accounted for 0.3% (4 out of 1,160) of all hospitalizations
among HIV mono-infected persons.
The increased risk for hospitalization due to psychiatric disease in the HIV/HCV
co-infected group highlights the need for mental health care in this population. The most
common diagnosis among psychiatric admissions was depression. Although drug use
may play a role in hospitalizations related to depression, this finding is consistent with
existing evidence that HIV/HCV co-infection is associated with higher prevalence and
severity of neuropsychiatric disease than either infection alone16,31. Integrated mental
health and HIV care programs have been shown to improve rates of HIV viral
suppression, retention in care, substance abuse and psychiatric symptoms as well as
decrease hospitalization costs32,33. Co-location of mental health services may offer
particular benefit to the HIV/HCV co-infected population.
Interestingly, we did not observe significantly increased risk of hospitalization for
renal, cardiovascular or endocrine diagnoses among HIV/HCV co-infected patients,
despite evidence that morbidity and mortality related to such diagnoses are increased with
HCV co-infection 10-13. Our study may not have included enough cardiovascular events to
47
detect a statistically significant difference. Our adjusted relative risk estimates for renal
and endocrine hospitalizations, on the other hand, suggested no trend towards increased
hospitalizations for these diagnoses. One possible explanation for this discrepancy is that
these complications are being successfully managed in the outpatient setting and, while
present, are not contributing to excess hospitalizations.
Therapy directed against hepatitis B and/or hepatitis C has been shown to
decrease progression to cirrhosis among co-infected PLWH34. Further investigation is
needed to evaluate the effects of hepatitis therapy on all-cause and cause-specific
hospitalization rates. Treatment of HBV is common among PLWH, and more than 75%
of HIV/HBV co-infected patients in the HIVRN are prescribed agents with activity
against both HIV and HBV (Moore RD, Personal Communication on 31 May 2013).
Conversely, prior studies have reported treatment rates of only 20-40% for HCV in the
routine clinical care of PLWH, with less than half of these persons achieving sustained
virologic response35,36. With the development of more effective and better tolerated antiHCV medications, increased utilization of anti-HCV therapy among co-infected patients
is expected in the near future37,38. If hepatitis therapy decreases hospitalization rates, this
could provide an economic counterbalance to the high cost of treating hepatitis,
especially with the newest anti-HCV medications38,39.
A potential limitation of this study is the reliance on hepatitis C antibodies as
evidence of hepatitis C co-infection. Unfortunately, HCV RNA data were not available to
confirm chronic infection. The bias introduced by misclassifying patients who cleared
HCV viremia as being chronically infected would likely make the HIV mono-infected
and HIV/HCV co-infected groups appear more similar. Inferences made based on
48
significant differences between these groups should therefore be robust despite the
misclassification. Also, HIV co-infection decreases spontaneous clearance of HCV to
fewer than 10% of cases, so it is expected that most patients in this analysis with positive
anti-HCV were chronically infected with HCV40.
Use of ICD-9 codes to determine cause for hospitalization may be less accurate
than physician chart review, although validation studies within individual institutions
have suggested high concordance with chart review21. Hospitalizations occurring outside
of each patient’s HIV care institution may not be completely captured, though efforts are
made by all HIVRN sites to capture utilization data from neighboring hospitals. The
inclusion of relatively few patients with HIV/HBV/HCV tri-infection in this study limits
our power to draw conclusions about this unique patient population. The population of
patients at HIVRN sites is not nationally representative and our findings may not be
generalizable to populations served by smaller clinics, located in more rural settings, or
cared for by providers with less HIV subspecialty experience.
This study demonstrates that chronic viral hepatitis is associated with increased
risk of hospitalization and therefore increased healthcare costs among PLWH. Policymakers and third-party payers should be aware of the heightened risk of hospitalization
associated with co-infection when allocating healthcare resources and considering models
of healthcare delivery. Our findings also underscore the importance of targeting patients
who are co-infected with HIV and viral hepatitis with preventive measures such as
routine vaccinations and integrated mental health services that may help to curb their
increased risk of hospitalization. Further investigation is needed to evaluate the effects of
therapy against hepatitis on hospitalization rates.
49
ACKNOWLEDGMENTS
HIVRN Participating Sites
Alameda County Medical Center, Oakland, California (Howard Edelstein, M.D.)
Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Richard Rutstein, M.D.)
Community Health Network, Rochester, New York (Roberto Corales, D.O.)
Drexel University, Philadelphia, Pennsylvania (Jeffrey Jacobson, M.D., Sara Allen,
C.R.N.P.)
Fenway Health, Boston, Massachusetts (Stephen Boswell, M.D.)
Johns Hopkins University, Baltimore, Maryland (Kelly Gebo, M.D., M.P.H., Richard
Moore, M.D., M.H.S., Allison Agwu, M.D., Sc.M.)
Montefiore Medical Group, Bronx, New York (Robert Beil, M.D.)
Montefiore Medical Center, Bronx, New York (Lawrence Hanau, M.D.)
Oregon Health and Science University, Portland, Oregon (P. Todd Korthuis, M.D.)
Parkland Health and Hospital System, Dallas, Texas (Ank Nijhawan, M.D., Muhammad
Akbar, M.D.)
St. Jude's Children's Hospital and University of Tennessee, Memphis, Tennessee (Aditya
Gaur, M.D.)
St. Luke's Roosevelt Hospital Center, New York, New York (Victoria Sharp, M.D.,
Stephen Arpadi, M.D.)
Tampa General Health Care, Tampa, Florida (Charurut Somboonwit, M.D.)
University of California, San Diego, California (W. Christopher Mathews, M.D.)
Wayne State University, Detroit, Michigan (Jonathan Cohn, M.D.)
Sponsoring Agencies
50
Agency for Healthcare Research and Quality, Rockville, Maryland (Fred Hellinger, Ph.D.,
John Fleishman, Ph.D., Irene Fraser, Ph.D.)
Health Resources and Services Administration, Rockville, Maryland (Robert Mills, Ph.D.,
Faye Malitz, M.S.)
Data Coordinating Center
Johns Hopkins University (Richard Moore, M.D., M.H.S., Jeanne Keruly, C.R.N.P.,
Kelly Gebo, M.D., M.P.H., Cindy Voss, M.A.)
51
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54
Table 2-1: Study Population Characteristics Stratified by Hepatitis Serostatus
Overall
n=12,819
2,793
HIV
Mono-infected
n=6,317 (49.3%)
1,160
HIV/HBV
Co-infected
n=532 (4.15%)
155
HIV/HCV
Co-infected
n=1,969 (15.4%)
762
HIV/HBV/HCV
Tri-infected
n=318 (2.48%)
82
Unknown
Serostatus
n=3,683 (28.7%)
634
18 – 34 [No. (%)]
47 (40-53)
1,908 (14.9)
45 (37-51)
1,244 (19.7)
46 (41-51)
56 (10.5)
50 (45-56)
93 (4.7)
48 (42-53)
25 (7.9)
47 (40-54)
490 (13.3)
35 – 49
6,112 (47.7)
3,147 (49.8)
304 (57.1)
785 (39.9)
158 (49.7)
1,718 (46.6)
50 – 64
4,348 (33.9)
1,708 (27.0)
152 (28.6)
1,032 (52.4)
127 (39.9)
1,329 (36.1)
451 (3.5)
218 (3.4)
20 (3.8)
59 (3.0)
8 (2.5)
146 (4.0)
Male
9,196 (71.7)
4.540 (71.9)
443 (83.3)
1,387 (70.4)
229 (72.0)
2,597 (70.5)
Female
3,623 (28.3)
1,777 (28.1)
89 (16.7)
582 (29.6)
89 (28.0)
1,086 (29.5)
White
3,420 (26.7)
1,860 (29.4)
201 (37.8)
429 (21.8)
86 (27.0)
844 (22.9)
Black
6,305 (49.2)
2,819 (44.6)
249 (46.8)
1,119 (56.8)
176 (55.4)
1,942 (52.7)
Hispanic
2,661 (20.8)
1,430 (22.7)
65 (12.2)
384 (19.5)
54 (17.0)
726 (19.7)
433 (3.4)
206 (3.3)
17 (3.2)
37 (1.9)
2 (0.63)
171 (4.6)
MSM
5,044 (39.3)
2,998 (47.4)
301 (56.6)
309 (15.7)
83 (26.1)
1,353 (36.7)
Heterosexual
4,824 (37.6)
2,656 (42.0)
181 (34.0)
424 (21.5)
123 (38.7)
1,440 (39.1)
IDU
2,225 (17.4)
313 (5.0)
23 (4.3)
1,169 (59.4)
104 (32.7)
616 (16.7)
726 (5.7)
350 (5.5)
27 (5.1)
67 (3.4)
8 (2.5)
274 (7.4)
Characteristic
Total hospitalizations in 2010 [No.]
Age on July 1, 2010 [years]
Median (IQR)
≥ 65
Gender [No. (%)]
Race/Ethnicity [No. (%)]
Other/unknown
HIV risk factor* [No. (%)]
Other/unknown
55
First CD4 count in 2010 (cells/mm3)
Median (IQR)
< 50 [No. (%)]
446 (268-645)
515 (4.0)
454 (278-648)
254 (4.0)
407 (231-622)
31 (5.8)
400 (233-607)
81 (4.1)
405 (220-642)
13 (4.1)
464 (289-673)
136 (3.7)
51-200
1,657 (12.9)
749 (11.9)
79 (14.8)
328 (16.7)
59 (18.6)
442 (12.0)
201-500
5,223 (40.7)
2,594 (41.1)
216 (40.6)
841 (42.7)
118 (37.1)
1,454 (39.5)
5,424 (42.3)
2,720 (43.1)
206 (38.7)
719 (36.5)
128 (40.2)
1,651 (44.8)
8,637 (67.4)
4,190 (66.3)
369 (69.4)
1,299 (66.0)
219 (68.9)
2,560 (69.5)
3,827 (29.8)
1,949 (30.8)
153 (28.8)
616 (31.3)
94 (29.6)
1,111 (28.8)
355 (2.8)
178 (2.8)
10 (1.9)
54 (2.7)
5 (1.6)
108 (2.9)
Yes
11,171 (87.1)
5,541 (87.7)
490 (92.1)
1,725 (87.6)
296 (93.1)
3,119 (84.7)
No
1,182 (9.2)
588 (9.3)
32 (6.0)
206 (10.5)
21 (6.9)
334 (9.1)
466 (3.6)
188 (3.0)
10 (1.9)
38 (1.9)
0 (0)
230 (6.2)
Medicaid
4,212 (32.8)
1,923 (30.4)
166 (31.2)
852 (43.3)
95 (29.9)
1,176 (31.9)
Medicare/dual eligible
2,655 (20.7)
1,217 (19.3)
117 (22.0)
427 (21.7)
89 (28.0)
805 (21.9)
Private
2,818 (22.0)
1,367 (21.6)
109 (20.5)
356 (18.1)
32 (10.1)
954 (25.9)
Ryan White/uninsured
2,537 (19.8)
1,497 (23.7)
122 (22.9)
287 (14.6)
79 (24.8)
552 (15.0)
>500
First HIV-1 RNA in 2010 (copies/mL)
[No. (%)]
< 400
≥ 400
Unknown
ART** [No. (%)]
Unknown
Insurance [No. (%)]
Unknown
597 (4.7)
313 (5.0)
18 (3.4)
47 (2.4)
23 (7.2)
196 (5.3)
IQR interquartile range; MSM men who have sex with men; IDU injection drug use; ART antiretroviral therapy
*HIV risk factors were considered mutually exclusive; subjects who reported IDU in addition to any other risk factor were categorized as IDU, men who reported sex
with men and women were categorized as MSM
**ART was defined as concurrent use of 3 or more antiretroviral medications from at least 2 classes at any time during calendar year 2010
56
Table 2-2: Univariate and Multivariate Analyses of Risk Factors for All-Cause
Hospitalization
Characteristic
IRR (95% CI)
Adjusted IRR (95% CI)
Hepatitis co-infection status
1.0
(Ref)
1.0
(Ref)
HIV mono-infection
1.81 (1.35-2.44)
1.55 (1.17-2.06)
HIV/HBV co-infection
2.10 (1.78-2.48)
1.45 (1.21-1.74)
HIV/HCV co-infection
1.44
(0.99-2.12)
1.52 (1.04-2.22)
HIV/HBV/HCV tri-infection
0.93 (0.81-1.08)
1.06 (0.90-1.24)
Unknown serostatus
Age (years)
1.0
(Ref)
1.0
(Ref)
18-34
1.08 (0.89-1.30)
0.93 (0.77-1.12)
35-49
1.21 (0.99-1.48)
1.40 (1.16-1.71)
50-64
1.93 (1.36-2.74)
1.92 (1.37-2.68)
≥65
Gender
1.0
(Ref)
1.0
(Ref)
Male
1.36 (1.19-1.55)
1.41 (1.22-1.64)
Female
Race
1.0
(Ref)
1.0
(Ref)
White
1.06 (0.90-1.26)
1.33 (1.14-1.54)
Black
1.19
(0.99-1.43)
0.92 (0.76-1.12)
Hispanic
Other/Unknown
0.58 (0.38-0.88)
0.57 (0.38-0.87)
HIV transmission risk factor*
MSM
1.0
(Ref)
1.0
(Ref)
Heterosexual
0.99 (0.83-1.17)
1.30 (1.13-1.50)
2.29 (1.94-2.70)
1.37 (1.13-1.66)
IDU
Other/Unknown
1.70 (1.30-2.22)
1.41 (1.08-1.84)
First CD4 count in 2010(cells/mm3)
1.0
(Ref)
1.0
(Ref)
>500
1.72 (1.50-1.97)
1.67 (1.45-1.92)
201-500
3.85 (3.24-4.58)
3.57 (2.99-4.28)
51-200
8.39
(6.51-10.81)
8.14
(6.27-10.58)
<50
First HIV-1 RNA in 2010 (copies/ml)
1.0
(Ref)
1.0
(Ref)
<400
2.04 (1.80-2.31)
1.26 (1.10-1.44)
≥400
ART**
Yes
1.0
(Ref)
1.0
(Ref)
No
0.89 (0.72-1.11)
1.04 (0.84-1.30)
Unknown
1.31 (0.86-1.99)
1.52 (1.12-2.07)
Insurance
1.0
(Ref)
1.0
(Ref)
Medicaid
0.87 (0.74-1.02)
0.99 (0.84-1.17)
Medicare/Dual eligible
0.48
(0.40-0.56)
0.55
(0.45-0.66)
Private
0.38 (0.32-0.46)
0.51 (0.42-0.62)
Ryan White/Uninsured
57
0.41 (0.29-0.56)
0.62 (0.44-0.87)
Unknown/Missing
IRR incidence rate ratio; CI confidence interval; MSM men who have sex with men; IDU
injection drug use; HIV human immunodeficiency virus; HBV hepatitis B virus; HCV
hepatitis C virus
Incidence rate ratios and 95% confidence intervals were calculated using negative binomial
regression. The adjusted model included the listed characteristics as well as an indicator
variable for clinical care site. IRRs in bold are statistically significant (p≤0.05).
*HIV risk factors were considered mutually exclusive; subjects who reported IDU in
addition to any other risk factor were categorized as IDU, men who reported sex with men
and women were categorized as MSM
**ART was defined as concurrent use of 3 or more antiretroviral medications from at least
2 classes at any time during calendar year 2010
58
Table 2-3: Most Common Individual Diagnoses Within Diagnostic Categories
Overall
n=2,793 (%)
637 (22.8)
130 (20.4)
92 (14.4)
88 (13.8)
HIV
Mono-infected
n=1,160 (%)
245 (21.1)
46 (18.8)
38 (15.5)
26 (10.6)
HIV/HBV
Co-infected
n=155 (%)
44 (28.4)
7 (15.9)
9 (20.4)
6 (13.6)
HIV/HCV
Co-infected
n=762 (%)
183 (24.0)
47 (25.7)
24 (13.1)
33 (18.0)
HIV/HBV/HCV
Tri-infected
n=82 (%)
22 (26.8)
3 (13.6)
5 (22.7)
1 (4.55)
Unknown
Serostatus
n=634 (%)
143 (22.6)
27 (18.9)
16 (11.2)
22 (15.4)
Cardiovascular
Chest pain
Heart failure
CAD/MI
290 (10.4)
57 (19.7)
54 (18.6)
43 (14.8)
114 (9.83)
17 (14.9)
29 (25.4)
13 (11.4)
7 (4.52)
1 (14.3)
0 (0)
1 (14.3)
94 (12.3)
28 (29.8)
9 (9.57)
14 (14.9)
11 (13.4)
1 (9.09)
8 (72.73)
0 (0)
64 (10.1)
10 (15.6)
8 (12.5)
15 (23.4)
Gastrointestinal/Liver
Complication of cirrhosis*
Pancreatitis
Diarrhea
265 (9.49)
45 (17.0)
41 (15.5)
40 (15.1)
97 (8.36)
4 (4.12)
10 (10.3)
23 (23.7)
22 (14.2)
10 (45.4)
0 (0)
2 (9.09)
77 (10.1)
18 (23.4)
20 (26.0)
7 (9.09)
5 (6.10)
0 (0)
1 (20.0)
1 (20.0)
64 (10.1)
13 (20.3)
10 (15.6)
7 (10.9)
AIDS-Defining Illness
Pneumocystis jiroveci
Cryptococcus
Recurrent bacterial pneumonia
217 (7.77)
59 (27.2)
33 (15.2)
21 (9.68)
113 (9.74)
36 (31.9)
22 (19.5)
10 (8.85)
15 (9.68)
3 (20.0)
1 (6.67)
2 (13.3)
41 (5.38)
10 (24.4)
9 (22.0)
7 (17.1)
7 (8.54)
0 (0)
0 (0)
0 (0)
41 (6.47)
10 (24.4)
1 (2.44)
2 (4.88)
Renal
Acute renal failure
Hypertension with chronic kidney disease
Urinary calculus
191 (6.84)
125 (65.4)
8 (4.19)
8 (4.19)
78 (6.72)
49 (62.8)
5 (6.41)
3 (3.85)
12 (7.74)
8 (66.7)
0 (0)
0 (0)
53 (6.96)
36 (67.9)
2 (3.77)
1 (1.89)
1 (1.22)
0 (0)
0 (0)
1 (100)
47 (7.41)
32 (68.1)
1 (2.13)
3 (6.38)
Psychiatric
Depression
Drug abuse/withdrawal
Psychosis/schizophrenia
169 (6.05)
44 (26.0)
40 (23.7)
34 (20.1)
63 (5.43)
15 (23.8)
13 (20.6)
15 (23.8)
6 (3.87)
2 (33.3)
0 (0)
1 (16.7)
52 (6.82)
21 (40.4)
12 (23.1)
6 (11.5)
2 (2.44)
1 (50.0)
1 (50.0)
0 (0)
46 (7.26)
5 (10.9)
14 (30.4)
12 (26.1)
Diagnostic Category
Common Diagnoses
Non-AIDS-Defining Infection
Bacterial pneumonia
Sepsis/bacteremia
Cellulitis
59
Pulmonary
Asthma/COPD
Acute respiratory failure
Pleural effusion
157 (5.62)
71 (45.2)
31 (19.8)
8 (5.10)
59 (5.09)
25 (42.4)
10 (17.0)
6 (10.2)
4 (2.58)
0 (0)
1 (25.0)
0 (0)
42 (5.51)
21 (50.0)
9 (21.4)
1 (2.38)
5 (6.10)
0 (0)
1 (20.0)
0 (0)
47 (7.41)
25 (53.2)
10 (21.3)
1 (2.13)
Endocrine
Electrolyte abnormalities
Diabetes
Cachexia
145 (5.19)
62 (42.8)
40 (27.6)
9 (6.21)
76 (6.55)
32 (42.1)
16 (21.0)
6 (7.89)
5 (3.23)
2 (40.0)
3 (60.0)
0 (0)
37 (4.86)
14 (37.8)
14 (37.8)
2 (5.41)
6 (7.32)
2 (33.3)
2 (33.3)
1 (16.7)
21 (3.31)
12 (57.1)
5 (23.8)
0 (0)
Non-AIDS-Defining Cancer
Lymphoma
Liver cancer
Lung cancer
140 (5.01)
41 (29.3)
10 (7.14)
6 (4.29)
53 (4.57)
25 (47.2)
0 (0)
2 (3.77)
11 (7.10)
1 (9.09)
0 (0)
0 (0)
38 (4.99)
9 (23.7)
7 (18.4)
3 (7.89)
2 (2.44)
0 (0)
1 (50.0)
0 (0)
36 (5.68)
6 (16.7)
2 (5.56)
1 (2.78)
Injury/Poisoning
118 (4.22)
45 (3.88)
4 (2.58)
39 (5.12)
3 (3.66)
27 (4.26)
Device/procedure complications
35 (29.7)
17 (37.8)
1 (25.0)
12 (30.8)
0 (0)
5 (18.5)
Poisoning
32 (27.1)
7 (15.6)
1 (25.0)
15 (38.5)
1 (33.3)
8 (29.6)
Fracture
24 (20.3)
9 (20.0)
1 (25.0)
6 (15.4)
2 (66.7)
6 (22.2)
Diagnostic categories and individual diagnoses are listed in order of frequency in the overall study population. The number of hospitalizations during 2010 that were
associated with each diagnostic category and individual diagnosis are reported. The percentage listed for each diagnostic category represents the percentage of all
hospitalizations associated with that diagnostic category. The percentage listed for each individual diagnosis represents the percentage of hospitalizations within that
diagnostic category. See Appendix Table for ICD-9 codes used to identify each diagnosis.
CAD coronary artery disease; MI myocardial infarction; COPD chronic obstructive pulmonary disease
*Complication of cirrhosis includes admissions for cirrhosis, hepatic encephalopathy, portal hypertension, and ascites
60
Figure 2-1: Unadjusted Hospitalization Rates by Diagnostic Category
Rates are standardized as hospitalizations per 100 person-years of follow-up. Unadjusted negative binomial regression was performed
to construct 95% confidence intervals.
61
Figure 2-2: Adjusted Relative Risk of Hospitalization by Diagnostic Category.
Incidence rate ratios and 95% confidence intervals were calculated using negative
binomial regression and are interpreted as the relative rate of admissions compared to the
reference group (HIV mono-infection) after adjusting for age, gender, race, HIV risk
factor, CD4 count, HIV-1 RNA, ART, insurance and clinical care site.
62
CHAPTER 3:
IMPACT OF HEPATITIS CO-INFECTION ON HEALTHCARE
UTILIZATION AMONG PERSONS LIVING WITH HIV
Reprinted from:
Crowell TA, Berry SA, Fleishman JA, LaRue RW, Korthuis PT, Nijhawan AE, Moore
RD, Gebo KA, for the HIV Research Network. “Impact of Hepatitis Co-Infection on
Healthcare Utilization among Persons Living with HIV.” JAIDS: Journal of Acquired
Immune Deficiency Syndromes. 2015 Apr 1;68(4):425-31.
By permission of Wolters Kluwer Health, Lippincott Williams & Wilkins©
63
ABSTRACT
Hepatitis B (HBV) and hepatitis C (HCV) co-infection are increasingly important
sources of morbidity among HIV-infected persons. We determined associations between
hepatitis co-infection and healthcare utilization among HIV-infected adults at four U.S.
sites during 2006-2011. Outpatient HIV visits did not differ by hepatitis serostatus and
decreased over time. Mental health visits were more common among HIV/HCV coinfected persons than among HIV mono-infected (IRR 1.27 [1.08-1.50]). Hospitalization
rates were higher among all hepatitis-infected groups than among HIV mono-infected
(HIV/HBV IRR 1.23 [1.05-1.44], HIV/HCV 1.22 [1.10-1.36], HIV/HBV/HCV 1.31
[1.02-1.68]). These findings may inform the design of clinical services and allocation of
resources.
64
INTRODUCTION
With the passage of the Patient Protection and Affordable Care Act (ACA),
persons living with HIV (PLWH) in the United States can expect healthcare changes that
include expansion of insurance coverage, removal of lifetime coverage caps, shifting of
resources to community health centers, and incentives to improve care coordination.1
Updated reports of healthcare utilization by PLWH are needed to understand the
healthcare needs of this population and plan for changes.
In the U.S., 5-10% of PLWH are co-infected with hepatitis B virus (HBV) and
20-33% with hepatitis C virus (HCV). 2-13 Co-infected patients are at risk of hepatic and
extrahepatic complications.13-23 Viral hepatitis has emerged as a leading cause of
morbidity and mortality among PLWH.24,25 We hypothesized that healthcare utilization
among PLWH might differ according to hepatitis serostatus.
The purpose of this study is to characterize the impact of hepatitis co-infection on
utilization of primary HIV care, mental health, and inpatient services in a multi-site,
multi-state cohort of PLWH.
METHODS
Site Selection and Data Collection
The HIV Research Network (HIVRN) is a consortium of HIV care sites in 11 U.S.
cities. Demographic, laboratory, and treatment data are abstracted from clinical records,
de-identified, and consolidated into a uniform database. All sites routinely report primary
HIV care visits; four also reported mental health and inpatient visits by adult participants
65
from January 1, 2006, through December 31, 2011, and are therefore included in this
analysis.
Participants in this analysis were engaged in care during ≥1 year in the study
period, as defined by having ≥1 primary HIV care visit, CD4 count, and HIV-1 RNA.
The unit of analysis was the patient-year (PY). Institutional review boards at each site
and the data coordinating center approved the collection and use of these data for analysis
and publication.
Definitions of Variables
Hepatitis serostatus was assessed using HBV surface antigen and HCV antibody.
Positive results within six months of enrollment and all negative results were carried
backward. Results before July 1 were used to categorize hepatitis serostatus from that
year onward, while results after July 1 were used only for subsequent years. Data were
censored at the time of death, loss to follow-up, or end of study.
Clinical and demographic characteristics were assessed using previouslypublished definitions as summarized in Table 3-1.26 Time-dependent variables included
age, CD4, HIV-1 RNA, ART and insurance status. Race/ethnicity, gender and HIV
transmission risk factor were categorized by self-report. For secondary analyses, FIB-4
score and use of ART with HBV activity were also considered time-dependent.27
Outcomes
Primary HIV care visits were defined as visits to an HIV care provider, not
including visits to nurses or subspecialists within multidisciplinary HIV clinics. Mental
66
health visits were visits to a psychologist, psychiatrist or other mental health provider, not
including visits to substance abuse treatment programs such as methadone clinics. Any
non-hospice acute care inpatient visit was included. Mortality was assessed by local study
staff report.
Data Analysis
Unadjusted healthcare utilization rates were calculated using total number of
visits as the numerator and aggregate person-time as the denominator. Person-time was
accrued daily as a fraction of each calendar year, so participants contributed <1 year of
observation during the year of enrollment or death.
Number of primary HIV care, mental health, and inpatient visits were modeled
using negative binomial regression to estimate incidence rate ratios (IRRs). Age,
race/ethnicity, gender, HIV risk factor, CD4, HIV-1 RNA, ART, and insurance status
were pre-specified covariates of interest. Multivariable models also included categorical
indicators for clinical care site to control for site-specific variability and indicators for
calendar year to control for secular trends. Several secondary analyses were performed
including, 1) adding number of primary HIV care visits as a predictor for mental health
and inpatient visits; 2) evaluating the effects of FIB-4 score and use of ART with HBV
activity (tenofovir, lamivudine, or emtricitabine) among subjects with any hepatitis and
with HBV co-infection, respectively; and 3) investigating mortality using logistic
regression with variables from the primary models.
To account for multiple observations involving the same individual, all models
used generalized estimating equations, clustered on patient, with exchangeable working
67
correlation and robust variance estimators. A two-sided type I error of 5% was considered
statistically significant. All analyses were performed using Stata 12.0 (StataCorp LP,
College Station, TX, USA).
RESULTS
Demographic and Clinical Characteristics
A total of 15,927 participants contributed 49,061 person-years of observation time.
At study entry, 9,146 individuals (57.4%) had HIV mono-infection; 536 (3.4%)
HIV/HBV; 2,056 (12.9%) HIV/HCV; 115 (0.7%) HIV/HBV/HCV; and 4,074 (25.6%)
unknown hepatitis serostatus (Table 3-1). Of those with initially unknown serostatus, 89
participants later contributed person-time to the HIV/HBV co-infected group, 365
HIV/HCV co-infected, and 26 HIV/HBV/HCV tri-infected. The median age ranged from
40.4 years (IQR 32.6-47.0) in the HIV mono-infected group to 47.0 (42.0-51.9) years in
the HIV/HCV co-infected group. There were higher proportions of male patients in the
HIV/HBV (91.0%) and HIV/HBV/HCV groups (83.5%) than in the other hepatitis
serostatus groups. IDU was reported in 5.7% of HIV mono-infected and 6.3% of
HIV/HBV co-infected patients, but was reported in 60.8% of HIV/HCV and 63.5% of
HIV/HBV/HCV patients.
Healthcare Utilization and Serostatus
A total of 227,618 primary HIV care visits, 24,415 mental health visits and
13,761 inpatient visits were observed. Primary HIV care visit rates were similar across all
hepatitis serostatus categories, with an average across time for the full study cohort of
68
4.64 visits/PY (Table 3-3). Over the five-year study period, 23.0% of participants had at
least one mental health visit, including 21.2% of HIV mono-infected, 21.2% of
HIV/HBV co-infected, 34.9% of HIV/HCV co-infected, 30.7% of HIV/HBV/HCV triinfected and 18.3% of patients with unknown hepatitis serostatus (Table 3-4). Among all
participants, 33.0% experienced ≥1 inpatient visits during the study period, including
31.6% of those with HIV mono-infection, 37.7% with HIV/HBV co-infection, 46.2%
HIV/HCV co-infection, 49.3% HIV/HBV/HCV tri-infection and 24.6% unknown
hepatitis serostatus.
In multivariable analysis, there was no association between hepatitis serostatus
and number of primary HIV care visits (Table 3-2). Compared to the HIV mono-infected
group, patients with HIV/HCV co-infection had significantly higher mental health
utilization rates (IRR 1.27 [95% CI 1.08-1.50]). Inpatient utilization was higher in all
hepatitis co-infected groups than with HIV mono-infection (HIV/HBV 1.23 [1.05-1.44],
HIV/HCV 1.22 [1.10-1.36], HIV/HBV/HCV 1.31 [1.02-1.68]). Non-White race/ethnicity
was a predictor of decreased mental health utilization as compared to White
race/ethnicity. Age >50 years was associated with more primary HIV visits; age 35-64
with more mental health visits and age ≥65 with more hospitalizations. Private insurance
was associated with lower primary HIV, mental health, and inpatient utilization, as
compared to Medicaid, Medicare, and Ryan White/Uninsured.
Healthcare Utilization Over Time
Across all hepatitis serostatus groups, primary HIV care visits ranged from 4.55.5 visits/PY in 2006 and decreased to 4.0-4.6 visits/PY in 2011 (unadjusted P for
69
decreasing trend < 0.01 for most hepatitis serostatus groups, Figure 3-1). Mental health
utilization decreased from 42.1-116.7 visits/100 PY in 2006 to 21.5-67.7 visits/100 PY in
2011, with significant declines in all hepatitis serostatus groups except unknown. There
were statistically significant decreases in unadjusted inpatient utilization across all groups
except HIV/HBV/HCV tri-infection and unknown serostatus, from 24.2-72.4 visits/100
PY in 2006 to 19.8-34.1 visits/100 PY in 2011. In multivariable analysis, however,
inpatient utilization did not decline across time for the full sample (Table 3-2) or for any
hepatitis serostatus subgroup (Figure 3-1).
Secondary Analyses
When compared to 1-3 visits/year, increasing HIV primary care utilization was
independently associated with increased mental health (4-6 visits/year IRR 1.52 [1.411.64], ≥7 visits/year 2.56 [2.34-2.80]) and inpatient (4-6 visits/year 1.22 [1.15-1.30], ≥7
visits/year 2.43 [2.26-2.61]) utilization. When controlling for primary HIV care visits,
inferences about hepatitis serostatus and healthcare utilization were unchanged.
Among subjects with any hepatitis co-infection, increased FIB-4 score was
associated with more HIV primary care visits (FIB-4 ≥3.25 IRR 1.08 [1.03-1.13], as
compared to <1.45) and inpatient admissions (1.95 [1.76-2.16]), but no difference in
mental health visits (0.96 [0.78-1.19]). Among subjects with HBV co-infection, use of
ART with HBV activity was associated with significantly fewer outpatient HIV primary
care visits (0.83 [0.73-0.95]) and a trend towards fewer inpatient admissions (0.71 [0.481.05]), as compared to use of ART without HBV activity and after controlling for other
70
covariates. With few observations, the model for mental health utilization did not
converge when adjusting for use of ART with HBV activity.
In multivariable analysis, mortality was higher in all hepatitis co-infected groups
than with HIV mono-infection (HIV/HBV 1.91 [1.39-2.62], HIV/HCV 1.30 [1.05-1.62],
HIV/HBV/HCV 2.56 [1.70-3.85]).
DISCUSSION
Our study makes several important observations about healthcare utilization
among PLWH. First, there was no difference in primary HIV care utilization according to
hepatitis serostatus. Second, patients with HIV/HCV co-infection demonstrated higher
rates of mental health visits than any of the other groups examined. Finally, rates of
inpatient utilization were elevated across all hepatitis-infected categories as compared to
HIV mono-infection.
Hepatitis co-infection was not associated with increased utilization of primary
HIV care. It is possible that PLWH who are co-infected with viral hepatitis have
differences in utilization of other subspecialty services, such as gastroenterology or
hepatology, but data regarding subspecialty referrals were not available and further
investigation is warranted. Decreasing utilization of primary HIV care services over time
may be attributable to evolving guideline recommendations for less frequent monitoring
for patients with well-controlled HIV disease.28-30
HIV/HCV co-infected participants utilized more outpatient mental health services
than any other hepatitis serostatus group. Prior studies have reported that 3.2-8.8% of the
general U.S. population presents for ≥1 mental health visit per year.31,32 In our study,
71
12.5-16.0% of PLWH utilized mental health services during each calendar year,
underscoring the high burden of mental illness among PLWH. As in the general U.S.
population, non-White PLWH were less likely to utilize mental health services than were
those self-reporting White race/ethnicity, potentially reflecting cultural barriers to care or
other access issues.33 Healthcare delivery systems caring for PLWH must be prepared to
handle a high demand for mental health services, particularly among HIV/HCV coinfected patients.
We have previously shown that hepatitis co-infection was associated with
increased inpatient utilization during a single year (2010), and here we demonstrate that
this relationship has persisted over time.26 The association between higher FIB-4 score
and increased hospitalization rates suggests that hepatocellular dysfunction may directly
contribute to the risk of hospitalization in co-infected patients. Use of ART with activity
against HBV by persons with HBV co-infection may attenuate the risk of hospitalization.
This study has several potential limitations. First, we relied on HCV antibodies as
an indirect marker of HCV co-infection, since HCV RNA levels were not available.
However, spontaneous clearance of HCV occurs in less than 10% of PLWH.34 The
impact of HCV therapy was not evaluated, but prior studies have reported low treatment
rates in the routine care of co-infected PLWH.35-37 Substance abuse is associated with
both psychiatric disease and HIV/HCV co-infection, so it is possible that substance abuse
contributes to differences in mental health utilization. Treatment of drug addiction was
not assessed, but may play a particularly important role in the management of HIV/HCV
co-infected patients. HIV/HCV co-infected patients tended to be older than patients in
other hepatitis serostatus groups. While all multivariable models included age, residual
72
confounding may have contributed to the differences observed between groups. Lower
healthcare utilization among the privately insured raises the possibility that financial
differences across groups, such as variable influences of lost work time, may have
contributed to some of our observations. Finally, hepatitis serostatus data were not
available for all participants, potentially introducing bias if there has been differential
failure to capture this information.
Chronic viral hepatitis is associated with differences in mental health and
inpatient utilization among PLWH, but not primary HIV care visits. Decreases in primary
HIV care utilization over time among all PLWH likely reflect shifting treatment
paradigms. Third-party payers and policy-makers should be aware of the high mental
health service utilization by patients with HIV/HCV co-infection and heightened risk of
hospitalization among PLWH with any hepatitis co-infection as they design healthcare
delivery systems and allocate limited healthcare resources.
73
ACKNOWLEDGMENTS
HIVRN Participating Sites
Alameda County Medical Center, Oakland, California (Howard Edelstein, M.D.)
Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Richard Rutstein, M.D.)
Community Health Network, Rochester, New York (Roberto Corales, D.O.)
Drexel University, Philadelphia, Pennsylvania (Jeffrey Jacobson, M.D., Sara Allen,
C.R.N.P.)
Fenway Health, Boston, Massachusetts (Stephen Boswell, M.D.)
Johns Hopkins University, Baltimore, Maryland (Kelly Gebo, M.D., Richard Moore,
M.D., Allison Agwu M.D.)
Montefiore Medical Group, Bronx, New York (Robert Beil, M.D.)
Montefiore Medical Center, Bronx, New York (Lawrence Hanau, M.D.)
Oregon Health and Science University, Portland, Oregon (P. Todd Korthuis, M.D.)
Parkland Health and Hospital System, Dallas, Texas (Ank Nijhawan, M.D., Muhammad
Akbar, M.D.)
St. Jude's Children's Hospital and University of Tennessee, Memphis, Tennessee (Aditya
Gaur, M.D.)
St. Luke's Roosevelt Hospital Center, New York, New York (Victoria Sharp, M.D.,
Stephen Arpadi, M.D.)
Tampa General Health Care, Tampa, Florida (Charurut Somboonwit, M.D.)
University of California, San Diego, California (W. Christopher Mathews, M.D.)
Sponsoring Agencies
74
Agency for Healthcare Research and Quality, Rockville, Maryland (Fred Hellinger, Ph.D.,
John Fleishman, Ph.D., Irene Fraser, Ph.D.)
Health Resources and Services Administration, Rockville, Maryland (Robert Mills, Ph.D.,
Faye Malitz, M.S.)
Data Coordinating Center
Johns Hopkins University (Richard Moore, M.D., Jeanne Keruly, C.R.N.P., Kelly Gebo,
M.D., Cindy Voss, M.A., Nikki Balding, M.S.)
75
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78
Table 3-1: Population Demographic and Clinical Characteristics at Study Entry
Characteristic
Age [years]*
Median (IQR)
18 – 34
35 – 49
50 – 64
≥ 65
Race/Ethnicity
White
Black
Hispanic
Other/Unknown
Gender
Male
Female
HIV risk factor
Heterosexual
MSM†
IDU‡
Other/Unknown
CD4 count (cells/mm3) #
Median (IQR)
< 50
51-200
201-500
>500
HIV-1 RNA (copies/mL) #
Median (IQR)
<400
≥ 400
HIV
Mono-infected
n=9,146 (%)
HIV/HBV
Co-infected
n=536 (%)
HIV/HCV
Co-infected
n=2,056 (%)
HIV/HBV/HCV
Tri-infected
n=115 (%)
Unknown
Serostatus
n=4,074 (%)
40.4 (32.6-47.0)
2856 (31.2)
4738 (51.8)
1420 (15.5)
132 (1.4)
41.2 (35.3-46.8)
127 (23.7)
325 (60.6)
80 (14.9)
4 (0.8)
47.0 (42.0-51.9)
154 (7.5)
1167 (56.8)
720 (35.0)
15 (0.7)
45.4 (41.2-50.7)
6 (5.2)
79 (68.7)
30 (26.1)
0 (0)
43.0 (35.2-49.4)
996 (24.4)
2154 (52.9)
855 (21)
69 (1.7)
3180 (34.8)
3750 (41.0)
1907 (20.8)
309 (3.4)
202 (37.7)
252 (47.0)
61 (11.4)
21 (3.9)
627 (30.5)
1247 (60.6)
154 (7.5)
28 (1.4)
34 (29.6)
75 (65.2)
4 (3.5)
2 (1.7)
1637 (40.2)
1611 (39.5)
692 (17.0)
134 (3.3)
6929 (75.8)
2217 (24.2)
488 (91.0)
48 (9.0)
1468 (71.4)
588 (28.6)
96 (83.5)
19 (16.5)
3255 (79.9)
819 (20.1)
3475 (38.0)
4807 (52.6)
519 (5.7)
342 (3.7)
124 (23.1)
359 (67.0)
34 (6.3)
19 (3.5)
384 (18.7)
357 (17.4)
1251 (60.8)
64 (3.1)
14 (12.2)
26 (22.6)
73 (63.5)
2 (1.7)
1180 (29.0)
1936 (47.5)
776 (19.0)
182 (4.5)
357 (175-549)
1050 (11.5)
1510 (16.5)
3813 (41.7)
2773 (30.3)
300 (106-504)
94 (17.5)
112 (20.9)
194 (36.2)
136 (25.4)
321 (155-528)
212 (10.3)
428 (20.8)
857 (41.7)
559 (27.2)
247 (95-415)
18 (15.6)
32 (27.8)
45 (39.1)
20 (17.4)
383 (212-567)
331 (8.1)
625 (15.3)
1760 (43.2)
1358 (33.3)
4326 (UND-84200)
2837 (31.0)
6309 (69.0)
9465 (UND-99138)
170 (31.7)
366 (68.3)
2293 (UND-56350)
701 (34.1)
1355 (65.9)
2650 (UND-90563)
41 (35.6)
74 (64.4)
898 (UND-49700)
1364 (33.5)
2710 (66.5)
79
ART§
966 (23.7)
No
2163 (23.6)
100 (18.7)
542 (26.4)
21 (18.3)
3108 (76.3)
Yes
6983 (76.4)
436 (81.3)
1514 (73.6)
94 (81.7)
1857 (59.8)
With HBV activity
4615 (66.1)
306 (70.2)
1007 (66.5)
63 (67.0)
#
Insurance
684 (16.8)
Medicaid
1307 (14.3)
81 (15.1)
721 (35.1)
41 (35.6)
827 (20.3)
Medicare/Dual eligible
1556 (17.0)
104 (19.4)
386 (18.8)
33 (28.7)
538 (13.2)
Private
1345 (14.7)
81 (15.1)
129 (6.3)
6 (5.2)
1945 (47.7)
Ryan White/Uninsured
4796 (52.4)
263 (49.1)
789 (38.4)
35 (30.4)
80 (2.0)
Unknown/Missing
142 (1.6)
7 (1.3)
31 (1.5)
0 (0)
Year of study entry
2000 (49.1)
2006
4500 (49.2)
235 (43.8)
1110 (54.0)
66 (57.4)
422 (10.4)
2007
959 (10.5)
79 (14.7)
257 (12.5)
16 (13.9)
236 (5.8)
2008
955 (10.4)
62 (11.6)
253 (12.3)
14 (12.2)
253 (6.2)
2009
969 (10.6)
56 (10.4)
187 (9.1)
9 (7.8)
475
(11.7)
2010
1005 (11.0)
63 (11.8)
163 (7.9)
7 (6.1)
688 (16.9)
2011
758 (8.3)
41 (7.6)
86 (4.2)
3 (2.6)
Status at end of study
3755 (92.2)
Alive
8585 (93.9)
471 (87.9)
1748 (85.0)
86 (74.8)
319 (7.8)
Deceased
561 (6.2)
65 (12.1)
308 (15.0)
29 (25.2)
*Age was assessed on July 1 of the year of study entry.
†Patients who reported sex with both men and women were categorized as MSM.
‡Patients who reported IDU in addition to any other risk factor were categorized as IDU.
#
CD4, HIV-1 RNA, and insurance data are the first available for the calendar year of study entry. UND: undetectable
§
ART refers to concurrent use of ≥3 antiretroviral medications from ≥2 classes at any time during the calendar year of study entry. HBV activity is
defined by an ART regimen that contains tenofovir, lamivudine, or emtricitabine; percentages with HBV activity reflect the number prescribed one or
more of these agents divided by the number prescribed ART in each hepatitis serostatus category.
80
Table 3-2: Multivariable Analysis of Risk Factors for Healthcare Utilization
Characteristic
Hepatitis Serostatus
HIV mono-infection
HIV/HBV co-infection
HIV/HCV co-infection
HIV/HBV/HCV triinfection
Unknown serostatus
Age (years)*
18 – 34
35 – 49
50 – 64
≥ 65
Race/Ethnicity
White
Black
Hispanic
Other/Unknown
Gender
Male
Female
HIV risk factor
Heterosexual
MSM†
IDU‡
Other/Unknown
CD4 count (cells/mm3)#
>500
201-500
51-200
< 50
HIV-1 RNA (copies/mL)#
<400
≥400
ART§
No
Yes
Insurance#
Private
Medicaid
Medicare/Dual eligible
Ryan White/Uninsured
Unknown/Missing
Year
2006
2007
2008
Primary HIV Care
Visits
IRR (95% CI)
Mental Health
Visits
IRR (95% CI)
Inpatient
Visits
IRR (95% CI)
1.0 (REF)
1.02 (0.97-1.07)
1.02 (0.99-1.05)
1.0 (REF)
0.86 (0.69-1.06)
1.27 (1.08-1.50)
1.0 (REF)
1.23 (1.05-1.44)
1.22 (1.10-1.36)
0.93 (0.86-1.00)
0.82 (0.57-1.18)
1.31 (1.02-1.68)
1.01 (0.99-1.03)
1.24 (1.03-1.48)
0.93 (0.85-1.03)
1.0 (REF)
0.94 (0.92-0.96)
1.03 (1.00-1.06)
1.08 (1.02-1.15)
1.0 (REF)
1.26 (1.05-1.50)
1.34 (1.10-1.64)
1.08 (0.76-1.53)
1.0 (REF)
0.94 (0.86-1.02)
1.08 (0.97-1.20)
1.49 (1.21-1.83)
1.0 (REF)
1.04 (1.01-1.06)
1.04 (1.01-1.07)
0.98 (0.93-1.04)
1.0 (REF)
0.60 (0.53-0.69)
0.50 (0.41-0.60)
0.47 (0.33-0.68)
1.0 (REF)
1.07 (0.98-1.17)
1.03 (0.93-1.15)
0.77 (0.58-1.02)
1.0 (REF)
1.09 (1.07-1.12)
1.0 (REF)
1.54 (1.29-1.84)
1.0 (REF)
1.16 (1.06-1.26)
1.0 (REF)
1.02 (1.00-1.05)
1.04 (1.01-1.08)
0.98 (0.93-1.04)
1.0 (REF)
1.65 (1.41-1.93)
1.49 (1.24-1.79)
1.34 (0.58-3.10)
1.0 (REF)
0.90 (0.82-1.00)
1.28 (1.14-1.43)
1.54 (1.27-1.87)
1.0 (REF)
1.07 (1.05-1.08)
1.20 (1.18-1.23)
1.49 (1.44-1.54)
1.0 (REF)
0.96 (0.88-1.04)
0.95 (0.83-1.08)
0.80 (0.67-0.95)
1.0 (REF)
1.53 (1.41-1.65)
3.15 (2.86-3.47)
7.49 (6.79-8.26)
1.0 (REF)
1.21 (1.19-1.22)
1.0 (REF)
1.09 (1.00-1.18)
1.0 (REF)
1.71 (1.60-1.82)
1.0 (REF)
1.20 (1.18-1.23)
1.0 (REF)
1.10 (0.97-1.25)
1.0 (REF)
0.94 (0.86-1.03)
1.0 (REF)
1.17 (1.13-1.20)
1.16 (1.13-1.19)
1.22 (1.19-1.25)
0.69 (0.66-0.72)
1.0 (REF)
1.66 (1.42-1.94)
1.46 (1.24-1.72)
1.63 (1.36-1.95)
0.70 (0.55-0.88)
1.0 (REF)
1.82 (1.60-2.07)
1.75 (1.53-2.01)
1.21 (1.07-1.38)
0.65 (0.50-0.85)
1.0 (REF)
0.93 (0.91-0.95)
0.93 (0.91-0.94)
1.0 (REF)
1.07 (0.98-1.16)
1.12 (1.01-1.24)
1.0 (REF)
1.14 (1.05-1.24)
1.05 (0.97-1.14)
81
2009
2010
2011
0.93 (0.92-0.95)
0.93 (0.91-0.95)
0.85 (0.83-0.87)
0.90 (0.82-0.99)
0.71 (0.64-0.79)
0.66 (0.60-0.73)
1.20 (1.11-1.31)
1.17 (1.07-1.27)
1.06 (0.96-1.16)
*Age was assessed annually on July 1.
†Patients who reported sex with both men and women were categorized as MSM.
‡Patients who reported IDU in addition to any other risk factor were categorized as IDU.
#
CD4, HIV-1 RNA, and insurance data are the first available for each year.
§
ART refers to concurrent use of ≥3 antiretroviral medications from ≥2 classes at any time during the
calendar year.
The unit of analysis for all models was person-year. Incidence rate ratios (IRRs) are interpreted as the
relative number of visits compared to the reference group after adjusting for other listed characteristics and
clinical care site. Statistically significant results (p≤0.05) are shown in bold.
82
Table 3-3: Healthcare Utilization Rates by Hepatitis Serostatus.
Utilization Rate (Standard Error)
[N]
Hepatitis
Serostatus
2006
2007
2008
2009
2010
HIV Primary Care (Visits/PY)
HIV
5.03 (0.06) 4.60 (0.06) 4.69 (0.05) 4.63 (0.05) 4.48 (0.05)
mono-infection
[4500]
[4612]
[5038]
[5435]
[5956]
HIV/HBV
5.51 (0.30) 4.99 (0.26) 5.02 (0.24) 4.76 (0.23) 4.74 (0.22)
co-infection
[235]
[292]
[323]
[346]
[388]
HIV/HCV
5.18 (0.13) 4.90 (0.12) 5.14 (0.11) 5.18 (0.11) 5.10 (0.11)
co-infection
[1110]
[1251]
[1380]
[1468]
[1512]
HIV/HBV/HCV 4.63 (0.36) 4.32 (0.33) 4.79 (0.33) 4.48 (0.32) 4.68 (0.32)
tri-infection
[66]
[77]
[85]
[84]
[88]
Unknown
4.50 (0.08) 4.39 (0.09) 4.37 (0.10) 4.43 (0.10) 4.66 (0.10)
serostatus
[2000]
[1740]
[1540]
[1464]
[1704]
Mental Health (Visits/100 PY)
42.15
46.26
50.04
40.72
32.33
HIV
(2.53)
(2.61)
(2.61)
(2.27)
(1.93)
mono-infection
[4500]
[4612]
[5038]
[5435]
[5956]
46.36
41.11
36.82
35.29
31.40
HIV/HBV
(9.53)
(8.10)
(7.22)
(6.83)
(6.13)
co-infection
[235]
[292]
[323]
[346]
[388]
116.74
105.33
101.56
88.22
74.36
HIV/HCV co(9.43)
(8.39)
(7.89)
(7.07)
(6.36)
infection
[1110]
[1251]
[1380]
[1468]
[1512]
102.9
19.34
50.20
43.52
57.26
HIV/HBV/HCV
(26.78)
(10.76)
(16.52)
(15.53)
(17.20)
tri-infection
[66]
[77]
[85]
[84]
[88]
42.29
61.96
63.26
51.21
48.31
Unknown
(5.85)
(7.78)
(8.20)
(7.59)
(7.00)
serostatus
[2000]
[1740]
[1540]
[1464]
[1704]
83
2011
Overall
4.23 (0.05)
[6010]
4.23 (0.20)
[385]
4.63 (0.10)
[1430]
4.06 (0.30)
[80]
4.45 (0.09)
[2110]
4.59 (0.03)
[9146]
4.82 (0.12)
[636]
5.02 (0.06)
[2445]
4.49 (0.23)
[150]
4.47 (0.05)
[4074]
29.76
(1.82)
[6010]
27.26
(5.61)
[385]
67.72
(6.19)
[1430]
21.49
(10.85)
[80]
43.63
(6.14)
[2110]
39.51
(1.60)
[9146]
35.33
(5.58)
[636]
90.57
(4.46)
[2445]
47.7
(12.76)
[150]
51.16
(2.96)
[4074]
Inpatient (Visits/100 PY)
25.53
29.34
26.86
25.49
22.94
19.76
24.69
HIV
(1.54)
(1.63)
(1.50)
(1.40)
(1.27)
(1.16)
(0.80)
mono-infection
[4500]
[4612]
[5038]
[5435]
[5956]
[6010]
[9146]
37.09
37.34
34.14
29.67
35.93
22.90
32.25
HIV/HBV
(7.86)
(7.11)
(6.41)
(5.77)
(6.04)
(4.74)
(3.58)
co-infection
[235]
[292]
[323]
[346]
[388]
[385]
[636]
55.46
55.76
48.09
44.65
42.58
34.11
46.08
HIV/HCV
(4.16)
(3.91)
(3.48)
(3.22)
(3.08)
(2.81)
(2.01)
co-infection
[1110]
[1251]
[1380]
[1468]
[1512]
[1430]
[2445]
72.36
59.41
60.24
61.44
32.21
34.13
52.27
HIV/HBV/HCV
(19.20)
(16.12)
(15.47)
(15.78)
(11.03)
(11.69)
(8.43)
tri-infection
[66]
[77]
[85]
[84]
[88]
[80]
[150]
24.17
23.36
19.69
22.96
20.10
20.90
21.94
Unknown
(2.42)
(2.61)
(2.50)
(2.78)
(2.47)
(2.32)
(1.26)
serostatus
[2000]
[1740]
[1540]
[1464]
[1704]
[2110]
[4074]
Mean utilization rate was calculated by dividing the aggregate number of visits by the aggregate person-time for each hepatitis
serostatus and year combination. These rates were multiplied by 100 for mental health and inpatient visits. Poisson regression was
used to estimate standard errors of the mean, modeling utilization rate as a function of time, stratified by hepatitis serostatus. Standard
errors were scaled using square root of Pearson chi-squared-based dispersion.
84
Table 3-4: Percentage of Patients Utilizing Healthcare Services, by Hepatitis
Serostatus.
(A) Mental Health
Percentage of Patients With At Least 1 Mental Health Visit
(95% Confidence Interval)
Hepatitis
Serostatus
2006
2007
2008
2009
2010
2011
Ever
HIV
mono-infection
HIV/HBV
co-infection
HIV/HCV
co-infection
HIV/HBV/HCV
tri-infection
Unknown
serostatus
12.4
(11.5-13.4)
12.3
(8.1-16.6)
23.8
(21.3-26.3)
22.7
(12.5-32.9)
13.8
(12.2-15.2)
12.6
(11.6-13.5)
13.0
(9.1-16.9)
24.1
(21.7-26.4)
13.0
(5.4-20.6)
14.2
(12.6-15.9)
13.7
(12.7-14.6)
13.9
(10.1-17.7)
25.7
(23.4-28.0)
22.4
(13.4-31.3)
14.7
(13.0-16.5)
12.7
(11.8-13.5)
13.0
(9.4-16.6)
23.4
(21.2-25.5)
16.7
(8.6-24.7)
14.2
(12.4-16.0)
11.1
(10.3-11.9)
10.8
(7.7-13.9)
20.6
(18.6-22.7)
15.9
(8.2-23.6)
13.1
(11.5-14.8)
10.7
(9.9-11.5)
11.9
(8.7-15.2)
20.3
(18.3-22.4)
12.5
(5.2-19.8)
12.2
(10.8-13.6)
21.2
(20.3-22.0)
21.2
(18.0-24.4)
34.9
(33.0-36.8)
30.7
(23.2-38.1)
18.3
(17.1-19.5)
(B) Inpatient
Hepatitis
Serostatus
HIV
mono-infection
HIV/HBV
co-infection
HIV/HCV
co-infection
HIV/HBV/HCV
tri-infection
Unknown
serostatus
2006
13.5
(12.5-14.5)
20.8
(15.6-26.0)
25.5
(22.9-28.1)
28.8
(17.8-39.8)
14.4
(12.9-16.0)
Percentage of Patients With At Least 1 Inpatient Visit
(95% Confidence Interval)
2007
2008
2009
2010
2011
15.0
(13.9-16.0)
17.5
(13.1-21.8)
28.4
(25.9-30.9)
29.9
(19.6-40.2)
12.7
(11.1-14.3)
14.1
(13.2-15.1)
18.6
(14.3-22.8)
25.4
(23.1-27.7)
25.9
(18.8-38.3)
11.7
(10.1-13.3)
13.8
(12.9-14.7)
15.3
(11.5-19.1)
22.3
(20.2-24.5)
28.6
(18.8-38.3)
11.9
(10.2-13.5)
12.7
(11.9-13.6)
18.8
(14.9-22.7)
22.0
(19.9-24.0)
22.7
(13.9-31.6)
11.0
(9.5-12.4)
11.1
(10.3-11.8)
14.3
(10.8-17.8)
18.2
(16.2-20.2)
18.8
(10.1-27.4)
11.2
(9.8-12.5)
Ever
31.6
(30.6-32.5)
37.7
(34.0-41.5)
46.2
(44.2-48.2)
49.3
(41.2-57.4)
24.6
(23.3-25.9)
All study participants had at least one HIV primary care visit during each year of
observation. For each hepatitis serostatus group, annual percentages were calculated by
dividing the number of participants with at least one visit by the total number of
participants categorized into that hepatitis serostatus group during the calendar year. For
each hepatitis serostatus group, percentage of patients ever utilizing each healthcare
service was calculated by dividing the number of participants with at least one visit while
contributing person-time to that hepatitis serostatus group by the total number of
participants ever categorized in that hepatitis serostatus group.
85
Figure 3-1: Healthcare Utilization Rates by Hepatitis Serostatus.
Rates are standardized as hospitalizations per person-year of follow-up for primary HIV care visits
and per 100 person-years of follow-up for mental health and inpatient visits. Tests of trend were
performed using negative binomial regression with categorical calendar year indicators to detect
differences in utilization between start of study in 2006 and end of study in 2011. “NC” indicates that
model was non-convergent due to the small number of events in the HIV/HBV/HCV tri-infected
group.
86
CHAPTER 4:
HOSPITALIZATION RATES AND REASONS AMONG HIV ELITE
CONTROLLERS AND PERSONS WITH MEDICALLY
CONTROLLED HIV INFECTION
Reprinted from:
Crowell TA, Gebo KA, Blankson JN, Korthuis PT, Yehia BR, Rutstein RM, Moore RD,
Sharp V, Nijhawan AE, Mathews WC, Hanau LH, Corales RB, Beil R, Somboonwit C,
Edelstein H, Allen SL, Berry SA, for the HIV Research Network. “Hospitalization Rates
and Reasons among HIV Elite Controllers and Persons with Medically Controlled HIV
Infection.” The Journal of Infectious Diseases. Advance access published 2014 Dec 15,
doi: 10.1093/infdis/jiu809.
By permission of Oxford University Press and the Infectious Diseases Society of
America.
87
ABSTRACT
Background: Elite controllers spontaneously suppress HIV viremia, but also
demonstrate chronic inflammation that may increase risk of comorbidities. We compared
hospitalization rates and causes among elite controllers to those of immunologically
intact persons with medically controlled HIV.
Methods: For adults in care at 11 sites from 2005-2011, person-years with CD4 ≥350
cells/mm2 were categorized as medical control, elite control, low viremia or high viremia.
All-cause and diagnostic category-specific hospitalization rates were compared between
groups using negative binomial regression.
Results: We identified 149 (0.4%) elite controllers among 34,354 persons in care.
Unadjusted hospitalization rates among the medical control, elite control, low viremia
and high viremia groups were 10.5, 23.3, 12.6, and 16.9 per 100 person-years,
respectively.
After adjustment for demographic and clinical factors, elite control was associated with
higher rates of all-cause (adjusted incidence rate ratio 1.77 [95% CI 1.21-2.60]),
cardiovascular (3.19 [1.50-6.79]) and psychiatric (3.98 [1.54-10.28]) hospitalization than
was medical control. Non-AIDS-defining infections were the most common reason for
admission overall (24.1% of hospitalizations) but were rare among elite controllers
(2.7%), in whom cardiovascular hospitalizations were most common (31.1%).
Conclusions: Elite controllers are hospitalized more frequently than persons with
medically controlled HIV, and cardiovascular hospitalizations are an important
contributor.
88
BACKGROUND
Elite controllers represent a small but important subset of persons living with HIV
(PLWH) who suppress the virus and have delayed disease progression in the absence of
antiretroviral therapy (ART).1,2 Although prevalence of elite control is estimated at only
0.15-1.5% of PLWH, study of these persons provides insights into HIV pathogenesis and
potential mechanisms for new HIV therapies.3-6 Despite spontaneous and durable control
of HIV viremia, elite control is associated with chronic immune activation and low-grade
inflammation that exceeds the level seen in persons who achieve viral suppression via
ART.7-9
Chronic inflammation among PLWH has been linked to complications such as
cardiovascular disease, opportunistic infections, and neurologic disorders.10-13 Persistent
low-grade inflammation may therefore place elite controllers at higher risk of clinical
events than are persons whose HIV is controlled with ART. For example, prior studies
have demonstrated a high burden of coronary atherosclerosis upon radiographic screening
of elite controllers, but data on any association with clinical outcomes are lacking.14,15
The relative rarity of elite control makes it difficult to study clinical outcomes such as
disease events or hospitalizations in this population.
We used a multi-site, multi-state cohort of persons living with HIV to compare
hospitalization rates among elite controllers to immunologically intact persons with
medically controlled and uncontrolled HIV.
METHODS
Site Selection and Data Collection
89
The HIV Research Network (HIVRN) is a consortium that includes 12 sites
providing longitudinal adult HIV care in 10 U.S. cities. Sites abstract comprehensive data
from clinical records, de-identify these data and submit them to a data coordinating center
for integration into a uniform database. Eleven of the participating sites submit
hospitalization data and were able to participate in chart reviews for the purpose of this
study (5 Northeast, 3 West, and 3 South). Nine of these sites have academic affiliations
and two are community-based. Inclusion in this retrospective cohort study was restricted
to persons who were in active care (defined as having at least one outpatient primary HIV
care visit, one CD4 cell count, and one HIV-1 RNA during the calendar year) at these
sites between 2005 and 2011. All sites contributed data for all years, except for one site
which was not included in 2005 because of incomplete data.
The analysis was limited to persons considered immunologically intact. Personyears were excluded if they contained two consecutive CD4 measurements <350
cells/mm3 or any CD4 measurement <200 cells/mm3. If two consecutive measurements
<350 cells/mm3 spanned separate calendar years, both calendar years were excluded.
Participants could contribute additional observation time to the analysis after consistent
CD4 reconstitution to >350 cells/mm3 occurred. Nadir CD4 was not considered a
criterion for study participation and participants with CD4 reconstitution following any
CD4 nadir were eligible to contribute observation time during calendar years following
the first year of consistent CD4 reconstitution to >350 cells/mm3. All participants in this
study either provided informed consent for inclusion in the HIVRN research database or
a waiver of informed consent was granted by their local institutional review board.
Institutional review boards at each site and at the data coordinating center at Johns
90
Hopkins University approved the collection and use of these data for analysis and
publication.
HIV Control Status
Elite control was defined by at least 3 consecutive HIV-1 RNA measurements, on
separate days and spanning a period of at least 12 months, registering below the limit of
detection for the assay in the absence of any ART. This definition of elite control has
been used in several prior reports.3,16,17 Accrual of elite control observation time began
only after one full year of undetectable HIV-1 RNA levels in order to minimize
misclassification due to any missing ART data. (Laboratory data prior to 2005 were used
to establish status in 2005). During elite control, detectable HIV-1 RNA levels <1000
copies/mL were permissible as long as such episodes represented the minority of
measurements during the calendar year. The calendar year during which the elite control
period ends was not considered an elite control PY. Medical records of elite controllers
identified via this algorithm were manually reviewed to confirm elite control status.
Individuals were excluded from the analysis if medical record review was not possible.
Elite controllers were not eligible to contribute observation time to other HIV control
categories.
Individuals not identified as elite controllers could contribute observation time to
the following groups: medical control, low viremia, and/or high viremia. Medical control
was defined by at least 3 consecutive HIV-1 RNA measurements, on separate days and
spanning at least 12 months, which registered below the limit of detection for the assay,
while prescribed ART. Medical control began during the first qualifying year starting
91
with an undetectable HIV-1 RNA. Detectable HIV-1 RNA levels <1000 copies/mL were
permissible after establishing medical control if they represented a minority of
measurements during any calendar year.
Exploratory data analysis suggested a difference in hospitalization rates above and
below the HIV-1 RNA threshold of 1000 copies/mL. Therefore, low viremia was defined
by all HIV-1 RNA measurements in the calendar year falling below this threshold, but
not satisfying other criteria for medical control. All person-years with HIV-1 RNA
measurement(s) ≥1000 copies/mL were considered high viremia person-years. HIV
control status was assessed annually and participants who were not identified as elite
controllers could transition between other HIV control categories with each change in
calendar year. A sensitivity analysis was performed in which participants could only
contribute person-time to one HIV control status category, censoring data at the time of
transition from that category.
Covariates
Age was assessed annually on July 1. Race/ethnicity and gender were categorized
based on self-report. HIV transmission risk factors were divided into mutually exclusive
categories: injection drug use (IDU), men who have sex with men (MSM), heterosexual
transmission, or other/unknown. Individuals who reported IDU in addition to any other
risk factor were categorized as IDU. Men who reported sex with both men and women
were categorized as MSM. Hepatitis B surface antigen and hepatitis C antibody were
used to determine hepatitis status and this assessment was updated annually. Outpatient
HIV primary care visits were tallied annually. Insurance status, CD4 and HIV-1 RNA
92
were updated with the first available assessment for each calendar year of observation.
Participants with dual eligibility for Medicaid and Medicare were included in the
Medicare category.
Outcomes
The primary outcome was all-cause hospitalization, and this was ascertained
using admission and discharge dates that are reported by all HIVRN sites.
We also investigated cause-specific hospitalization rates within the subgroup of
nine HIVRN sites which had International Classification of Diseases 9th edition (ICD-9)
diagnosis code data available for each hospitalization. Hospitalizations were assigned to
one of 18 diagnostic categories using a previously published algorithm.18,19 First, the
primary diagnostic code was identified as the first-listed ICD-9 code that did not refer to
HIV (042, V08, 795.71, V01.79), chronic HBV (070.22, 070.23, 070.32, 070.33), chronic
HCV (070.44, 070.54, 070.70, 070.71), or oral candidiasis (112.0), since these represent
comorbidities frequently recorded for billing purposes but insufficient to justify
hospitalization. Second, Clinical Classifications Software (CCS) was used to assign the
primary ICD-9 code into one of 18 “first-level” CCS categories 20. Finally, we modified
the CCS diagnostic categories by reassigning infections (such as pneumonia) from organ
system categories to the infection category; combining the congenital, perinatal, and
unclassified categories (together representing 1% of admissions); and reassigning specific
infections and malignancies into a new AIDS-defining illness (ADI) category according
to Centers for Disease Control and Prevention criteria.21
93
Data Analysis
All-cause and cause-specific hospitalization rates were calculated using total
number of visits as the numerator and aggregate person-time as the denominator and
multiplied by 100 to obtain rates per 100 person-years (PY). Participants could contribute
<1 year of observation during a calendar year due to death or new enrollment in care.
Univariable and multivariable negative binomial regression models were used to estimate
incidence rate ratios (IRRs) for hospitalization rates associated with HIV control status,
age, race, sex, HIV risk factor, CD4 stratum, hepatitis status, number of primary HIV
care visits and insurance status. Multivariable models included indicators for clinical care
site to adjust for site-specific variability and for calendar year to adjust for secular trends.
All models used generalized estimating equations, clustered on person, with
unstructured working correlation, robust variance estimators, and an offset for persontime. This technique adjusts the variance to account for multiple hospitalization events by
a single person, including when these events occur under different exposure categories
(e.g. under low viremia in one year and medical control in a separate year).22 A two-sided
type I error of 5% was considered statistically significant. All analyses were performed
using Stata 12.0 (StataCorp LP, College Station, TX, USA).
RESULTS
Of 34,354 persons actively engaged in care during the study period, 23,461
satisfied the CD4 criterion for inclusion in this analysis and contributed 64,290 personyears of observation. Algorithmically, 188 persons were identified as elite control
candidates in the HIVRN database. Of these, 17 were excluded from the analysis because
94
they did not have clinical records available for review and 22 were excluded because
manual record review revealed they were prescribed ART. Ultimately, 149 chartconfirmed elite controllers contributed 369 person-years of observation time. The
prevalence of elite control is therefore estimated to be at least 0.43% of the full
population of 34,354 persons.
Among the remaining 23,273 participants, 9,226 contributed 26,176 PY of
medical control, 12,044 contributed 17,313 PY of low viremia, and 12,847 contributed
20,414 PY of high viremia observation time. At study entry, persons with elite control
were more likely than those with medical control to be female (50.3% vs. 25.7%,
p<0.001) and Black (58.4% vs. 40.9%, p<0.001) (Table 4-1). CD4 counts at study entry
were higher in the elite control group (778 cells/mm3 [580-961]) than in the medical, low
viremia and high viremia groups (481 [396-640]; 510 [401-677]; 482 [384-634],
respectively, overall p<0.001).
There were 8,456 hospitalizations among all participants. The percentage of
participants ever hospitalized during time accrued in each HIV control group was 14.6%
of persons during medical control, 25.5% during elite control, 10.6% during low viremia
and 15.0% during high viremia. Overall, the elite control group had the highest all-cause
hospitalization rate, 23.3 hospitalizations per 100 PY (range 15.4-27.9), followed by the
high viremia (16.9, range 14.1-18.8), low viremia (12.6, range 12.0-13.7), and medical
control (10.5, range 8.6-12.2) groups (Figure 4-1).
In our multivariable model, elite control was associated with a higher
hospitalization rate than medical control (adjusted incidence rate ratio [aIRR] 1.77 [95%
CI 1.21-2.60]) (Table 4-2). Hospitalization rates were also elevated in the high viremia
95
(1.71 [1.57-1.87]) and low viremia (1.34 [1.24-1.46]) groups, as compared to medical
control. Other factors independently associated with hospitalization included older age,
female gender, IDU, lower CD4 count, HIV/HCV co-infection, HIV/HBV/HCV triinfection, more outpatient visits, and Medicaid or Medicare insurance (as compared to
private insurance).
In the sensitivity analysis in which participants were censored at the time of
transition from their first recorded HIV control category, elite control was again
associated with a higher hospitalization rate than was medical control in both unadjusted
(IRR 2.69 [1.49-4.85]) and adjusted (aIRR 2.02 [1.24-3.28]) models.
Among the subgroup of nine clinical care sites with available ICD-9 data, 5,593
total hospitalizations were observed (Table 4-3). Overall, non-AIDS defining infections
were the most common reason for hospitalization, representing 24.1% of admissions. In
the elite control group, however, non-AIDS-defining infections accounted for just 2.7%
of admissions. Conversely, cardiovascular disease was the reason for 13.5% of
admissions overall but was more common among elite controllers, accounting for 31.1%
of admissions. Among elite controllers, the most common diagnoses leading to
cardiovascular hospitalization were chest pain (26.1%), coronary artery disease (13.0%)
and heart failure (13.0%). Pulmonary disease accounted for 4.8% of admissions overall
but 21.6% of admissions in the elite control group. Among elite controllers, the most
common diagnosis leading to pulmonary hospitalization was asthma/COPD (87.5%).
Two hospitalizations for AIDS-defining-illness occurred in the elite control group, one
for Kaposi’s sarcoma and one for Mycobacterium tuberculosis.
96
Multivariable models were used to explore factors associated with all-cause and
diagnostic category-specific hospitalization for the five most common diagnostic
categories at sites with available ICD-9 data (Table 4-4). Inferences about factors
associated with all-cause hospitalization were similar to those in the overall study cohort,
including a similarly heightened rate among persons with elite control as compared to
medical control (aIRR 1.99 [1.29-3.06]). The cardiovascular hospitalization rate was
significantly higher among elite controllers (3.19 [1.50-6.79]) than in the medical control
group. Psychiatric hospitalization rates were higher in the elite control (3.98 [1.5410.28]), low viremia (1.65 [1.15-2.37]) and high viremia (3.14 [2.35-4.21]) groups than
in the medical control group. Compared to medical control, elite control was associated
with a trend toward a lower non-AIDS-defining infection hospitalization rate (aIRR 0.32
[0.08-1.30]) whereas higher rates were seen in the low viremia (1.57 [1.31-1.88]) and
high viremia (2.48 [2.10-2.93]) groups.
There was a single participant in the elite control group with 21 hospitalizations
over the study period. A sensitivity analysis was performed excluding this person. In this
analysis, the all-cause hospitalization rate in the elite control group was attenuated, but
remained significantly greater than in the medical control reference group (aIRR 1.56
[1.09-2.24]). Many of this person’s admissions were related to asthma/COPD, and in the
sensitivity analysis pulmonary admissions accounted for just 3.8% of admissions in the
elite control group, similar to the overall study population (4.5%). Cardiovascular disease
remained the most common reason for admission among elite controllers (30.2%).
DISCUSSION
97
This study identified an increased rate of hospitalization among persons with elite
control of HIV as compared to medical control. Furthermore, elite control was associated
with a higher rate of cardiovascular hospitalization, which was the most common type of
hospitalization among elite controllers. Non-AIDS-defining infections, while a common
reason for admission in the overall study population, were relatively rare among elite
controllers. Finally, female gender was associated with elite control.
To date, data on hospitalizations among elite controllers are scarce. Two previous
studies reporting no increased hospitalization risk for elite controllers both included only
25 elite controllers and examined hospitalization as a secondary outcome.3,23
We are unaware of previous data specifically evaluating cardiovascular
hospitalizations among elite controllers. Prior studies have shown increased prevalence of
atherosclerotic plaques among elite controllers as compared to HIV-negative controls, but
not as compared to PLWH treated with ART.14,15 Additionally, elite controllers have been
shown to have elevated levels of activated CD8+ T cells, D-dimer, soluble tissue factor,
and interferon gamma-induced protein 10 levels as compared to both HIV-seronegative
persons and persons with medically controlled HIV.7-9 The cause of immune activation in
elite controllers is unclear, but the presence of replication-competent virus,24 persistent
low-level viremia,25 ongoing low level viral replication,26-28 and the vigorous immune
response that controls this viral replication29 may be important factors. These
pathophysiologic factors could contribute to higher hospitalization rates for
cardiovascular disease among elite controllers.
If immune system activation is a contributor to the excess rate of cardiovascular
and other hospitalizations among elite controllers, then ART and/or anti-inflammatory
98
medicines such as statins or aspirin may be beneficial for this group. Prior studies have
demonstrated that ART can decrease T cell activation28,30-32 and/or increase CD4
counts33,34 among elite controllers. In our cohort, only 10 participants known to be elite
controllers were ever exposed to ART, so we could not effectively evaluate differences in
hospitalization. Prospectively assessing the impact of ART and/or anti-inflammatory
medicines on hospitalization among elite controllers and understanding the potential role
of chronic inflammation would inform the clinical care of elite controllers and may
inform research on interventions that aim to induce an elite control state, such as some
candidate HIV vaccines.
Non-AIDS-defining infections accounted for relatively few hospitalizations
among elite controllers in our study (2.7%), despite being the most common reason for
admission overall (24.1%). Elite control is characterized by a variety of unique
immunologic parameters, including differences in class I antigen presenting molecules
and CD8+ T-cell populations that may also plausibly impact susceptibility to other
infectious diseases.29 Future studies should evaluate immunologic response to bacterial
and other pathogens among elite controllers in order to clarify any protective mechanisms
that contribute to the low rate of non-AIDS-defining infections in this population.
We are uncertain why elite controllers were hospitalized more frequently for
psychiatric conditions. This association may warrant investigation in other cohorts.
Elite controllers in our study were more likely to be female than were persons in
other HIV control categories. This is consistent with trends seen in several other studies
of elite controllers, though in most studies the gender difference was not statistically
significant.3,7,17,35-38 This is also consistent with prior observations that women tend to
99
have lower HIV-1 RNA levels than men, particularly within the first year after
seroconversion.39 Few prior studies contained both an appropriate comparison group of
non-elite-controllers and sufficient power to detect a gender difference in the prevalence
of elite control. If a gender difference is confirmed in additional studies, it may provide
clues to the immunologic mechanisms underlying elite control.
Our study also provides an updated estimate of the prevalence of elite control
among PLWH. Our estimate of 0.43% is within the range suggested by other studies.3-5 It
is likely that this number slightly underestimates the true prevalence, since some potential
elite controllers were excluded from the analysis because manual review of medical
records was not possible.
A major strength of our study is its large sample of elite controllers. Among
published cohorts, only the International HIV Controller Consortium reports a larger
sample of elite controllers.17 A limitation of our study is the potential for selection bias
due to our data being limited to persons actively engaged in HIV care. Because of no
perceived need for ART, it is possible that elite controllers might be less likely than other
PLWH to be engaged in care and that elite controllers who have additional serious
medical conditions are preferentially captured in our cohort. This could contribute to an
increased hospitalization rate as compared to members of the cohort whose only serious
medical condition may be HIV. However, our study captured 93% (14 of 15) of the elite
controllers otherwise known to local investigators at two clinical care sites with unrelated
ongoing studies of elite controllers, suggesting both good sensitivity of our study
definition for elite control and minimal impact of selection bias. Duration of HIV
infection may influence hospitalization rates, but this information was not available for
100
participants in this cohort. Hospitalizations occurring outside of each participant’s HIV
care institution may not have been completely captured, though we would not expect this
to have a differential impact by HIV control status. This study was limited to personyears with high CD4 counts in order to provide meaningful comparators to the primary
group of interest, elite controllers, who by nature of their disease process tend to have
preserved CD4 counts. The medical control population does not necessarily reflect the
full spectrum of patients seen routinely in HIV clinics, which may, at any given time,
include a subset with low CD4.
Another limitation is our lack of data on smoking, which are not routinely
collected in this cohort but represent an important risk factor for all-cause and
cardiovascular hospitalization. In general, smoking rates among PLWH are high, with 4667% categorized as current smokers by recent estimates.40-44 We conducted a focused
chart review to investigate smoking within our study population. All elite controllers
were randomly matched with up to four persons with medical control based on clinical
care site, gender, race, and age in five-year bands. Records of the 149 elite controllers
and of 581 matched persons with medical control were then searched to determine ever
vs. never smoking status. Data were available for 134 elite controllers and 555 persons
with medical control. We found that 110/134 (82%) elite controllers and 377/555 (68%)
persons with medical control had ever smoked (p=0.001). Smoking was thus common in
both groups, and while the higher rate among elite controllers may have contributed to
more hospitalizations, it is unlikely to explain the near doubling in adjusted all-cause
hospitalization rate or the tripling in cardiovascular hospitalization rate associated with
elite control vs. medical control.
101
This study demonstrates that elite control of HIV is associated with higher
hospitalization rate than is medical control of HIV using ART. Cardiovascular disease
appears to be a major driver of this association. Compared to other HIV-infected patients,
elite controllers had relatively few admissions due to non-AIDS-defining infections.
These findings could reflect clinical manifestations of ongoing immune activation in elite
controllers. Further investigations are needed to evaluate the mechanisms underlying
these associations and to clarify the potential benefit of ART and/or anti-inflammatory
agents in the management of elite controllers.
102
ACKNOWLEDGMENTS
HIVRN Participating Sites
Alameda County Medical Center, Oakland, California (Howard Edelstein, M.D.)
Children's Hospital of Philadelphia, Philadelphia, Pennsylvania (Richard Rutstein, M.D.)
Trillium Health, Rochester, New York (Roberto Corales, D.O.)
Drexel University, Philadelphia, Pennsylvania (Jeffrey Jacobson, M.D., Sara Allen,
C.R.N.P.)
Fenway Health, Boston, Massachusetts (Stephen Boswell, M.D.)
Johns Hopkins University, Baltimore, Maryland (Kelly Gebo, M.D., Richard Moore,
M.D., Allison Agwu M.D.)
Montefiore Medical Group, Bronx, New York (Robert Beil, M.D.)
Montefiore Medical Center, Bronx, New York (Lawrence Hanau, M.D.)
Oregon Health and Science University, Portland, Oregon (P. Todd Korthuis, M.D.)
Parkland Health and Hospital System, Dallas, Texas (Ank Nijhawan, M.D., Muhammad
Akbar, M.D.)
St. Jude's Children's Hospital and University of Tennessee, Memphis, Tennessee (Aditya
Gaur, M.D.)
St. Luke's Roosevelt Hospital Center, New York, New York (Victoria Sharp, M.D.,
Stephen Arpadi, M.D.)
Tampa General Health Care, Tampa, Florida (Charurut Somboonwit, M.D.)
University of California, San Diego, California (W. Christopher Mathews, M.D.)
Sponsoring Agencies
103
Agency for Healthcare Research and Quality, Rockville, Maryland (Fred Hellinger, Ph.D.,
John Fleishman, Ph.D., Irene Fraser, Ph.D.)
Health Resources and Services Administration, Rockville, Maryland (Robert Mills, Ph.D.,
Faye Malitz, M.S.)
Data Coordinating Center
Johns Hopkins University (Richard Moore, M.D., Jeanne Keruly, C.R.N.P., Kelly Gebo,
M.D., Cindy Voss, M.A., Nikki Balding, M.S.)
104
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108
Table 4-1: Demographic and Clinical Characteristics at Study Entry by HIV Control Status
Medical
Elite
Low
Control
Control
Viremia
n=4,709 (%)
n=149 (%)
n=7,998 (%)
Characteristic
Age [years]*
Median (IQR)
45.4 (39.4-51.7)
46.4 (40.5-53.2)
44.2 (37.4-50.5)
18 – 34
605 (12.8)
21 (14.1)
1558 (19.5)
35 – 49
2625 (55.7)
76 (51.0)
4297 (53.7)
50 – 64
1360 (28.9)
49 (32.9)
1974 (24.7)
≥ 65
119 (2.5)
3 (2.0)
169 (2.1)
Race/ethnicity
White
1566 (33.3)
35 (23.5)
2403 (30.0)
Black
1927 (40.9)
87 (58.4)
3374 (42.2)
Hispanic
1097 (23.3)
25 (16.8)
1968 (24.6)
Other/unknown
119 (2.5)
2 (1.3)
253 (3.2)
Gender
Male
3498 (74.3)
74 (49.7)
5976 (74.7)
Female
1211 (25.7)
75 (50.3)
2022 (25.3)
HIV risk factor
Heterosexual
1701 (36.1)
59 (39.6)
2763 (34.6)
MSM
2081 (44.2)
39 (26.2)
3618 (45.2)
IDU
782 (16.6)
45 (30.2)
1247 (15.6)
Other/unknown
145 (3.1)
6 (4.0)
370 (4.6)
3 **
CD4 count [cells/mm ]
Median (IQR)
481 (396-640)
778 (580-961)
510 (401-677)
200-350
347 (7.4)
1 (0.7)
830 (10.4)
351-500
2199 (46.7)
19 (12.8)
3030 (37.9)
501-750
1411 (30.0)
45 (30.2)
2679 (33.5)
>750
752 (16.0)
84 (56.4)
1459 (18.2)
109
High
Viremia
n=10,605 (%)
40.3 (31.0-47.4)
3690 (34.8)
5064 (47.8)
1727 (16.3)
124 (1.2)
2740 (25.8)
5331 (50.3)
2241 (21.1)
293 (2.8)
7410 (69.9)
3195 (30.1)
4074 (38.4)
4368 (41.2)
1714 (16.2)
449 (4.2)
482 (384-634)
1650 (15.6)
4044 (38.1)
3350 (31.6)
1561 (14.7)
HIV-1 RNA [copies/mL]**
Median (IQR)
Undetectable§
Undetectable§
Undetectable§
7640 (1124-36702)
†
Hepatitis status
HIV mono-infected
2397 (50.9)
55 (36.9)
3637 (45.5)
5289 (49.9)
HIV/HBV co-infected
157 (3.3)
2 (1.3)
216 (2.7)
240 (2.3)
HIV/HCV co-infected
497 (10.6)
34 (22.8)
790 (9.9)
1055 (10.0)
HIV/HBV/HCV tri-infected
55 (1.2)
2 (1.3)
105 (1.3)
150 (1.4)
Unknown
1603 (34.0)
56 (37.6)
3250 (40.6)
3871 (36.5)
Annual outpatient HIV visits
Median (IQR)
4 (3-7)
4 (2-7)
4 (2-6)
4 (2-6)
1-2
793 (16.8)
52 (34.9)
2224 (27.8)
3694 (34.8)
3-4
1659 (35.2)
36 (24.2)
2487 (31.1)
2932 (27.6)
5-6
950 (20.2)
18 (12.1)
1498 (18.7)
1698 (16.0)
≥7
1307 (27.8)
43 (28.9)
1789 (22.4)
2281 (21.5)
**
Insurance
Private
638 (13.6)
18 (12.1)
1109 (13.9)
1238 (11.7)
Medicaid
1358 (28.8)
69 (46.3)
2864 (35.8)
3629 (34.2)
Medicare/dual eligible
995 (21.1)
12 (8.0)
1206 (15.1)
1078 (10.2)
Ryan White/uninsured
1497 (31.8)
46 (30.9)
2564 (32.1)
4160 (39.2)
Unknown
221 (4.7)
4 (2.7)
255 (3.2)
50 (4.7)
Year of study entry
2005
1859 (39.5)
53 (35.6)
1501 (18.8)
2998 (28.3)
2006
1029 (21.8)
26 (17.4)
850 (10.6)
1615 (15.2)
2007
376 (8.0)
14 (9.4)
838 (10.5)
1149 (10.8)
2008
333 (7.1)
12 (8.0)
904 (11.3)
1227 (11.6)
2009
347 (7.4)
16 (10.7)
926 (11.6)
1031 (9.7)
2010
409 (8.7)
14 (9.4)
1003 (12.5)
1182 (11.2)
2011
356 (7.6)
14 (9.4)
1976 (24.7)
1403 (13.2)
Abbreviations: IQR, interquartile range; MSM, men who have sex with men; IDU, injection drug use; HBV, hepatitis B virus; HCV,
hepatitis C virus.
*
Age was assessed on July 1 of the year of study entry.
110
**
CD4, HIV-1, and insurance data are the first available for the calendar year of study entry.
Hepatitis B and C status were determined by determined by hepatitis B surface antigen and hepatitis C antibody, respectively.
Hepatitis status at study entry was assessed by positive tests before entry or within six months after entry, or by negative test result at
first testing any time after study entry.
§ Undetectable HIV-1 RNA refers to a level below the limit of detection for the assay used during routine clinical care, which may
have been 20, 40, 48, 50, 75, 80 or 400 copies/mL.
†
111
Table 4-2: Univariable and Multivariable Analyses of Factors Associated with AllCause Hospitalization
Unadjusted IRR
Adjusted IRR
Characteristic
(95% CI)
(95% CI)
HIV control status
Medical control
1.0 (Ref)
1.0 (Ref)
Elite control
2.08 (1.34-3.23)
1.77 (1.21-2.60)
Low viremia
1.18 (1.98-1.29)
1.34 (1.24-1.46)
High viremia
1.56 (1.43-1.69)
1.71 (1.57-1.87)
*
Age (years)
18-34
1.0 (Ref)
1.0 (Ref)
35-49
1.02 (0.91-1.14)
1.22 (1.09-1.36)
50-64
1.72 (1.53-1.93)
1.26 (1.12-1.42)
≥65
2.85 (2.35-3.45)
2.00 (1.64-2.44)
Race
White
1.0 (Ref)
1.0 (Ref)
Black
1.00 (0.90-1.12)
1.31 (1.19-1.44)
Hispanic
0.95 (0.84-1.07)
0.80 (0.71-0.91)
Other/unknown
0.68 (0.48-0.94)
0.69 (0.50-0.95)
Gender
Male
1.0 (Ref)
1.0 (Ref)
Female
1.54 (1.42-1.68)
1.31 (1.18-1.45)
HIV risk factor**
Heterosexual
1.0 (Ref)
1.0 (Ref)
MSM
0.66 (0.60-0.72)
0.88 (0.78-0.99)
IDU
1.60 (1.44-1.78)
1.19 (1.06-1.33)
Other/unknown
1.47 (1.20-1.82)
1.34 (1.08-1.66)
3 †
CD4 count (cells/mm )
>750
1.0 (Ref)
1.0 (Ref)
501-750
1.17 (1.07-1.28)
1.16 (1.06-1.27)
<500
1.39 (1.26-1.52)
1.33 (1.21-1.46)
‡
Hepatitis status
HIV mono-infected
1.0 (Ref)
1.0 (Ref)
HIV/HBV co-infected
1.07 (0.83-1.38)
1.12 (0.87-1.45)
HIV/HCV co-infected
2.08 (1.86-2.32)
1.35 (1.20-1.52)
HIV/HBV/HCV tri-infected
1.47 (1.03-2.09)
1.55 (1.09-2.18)
Unknown
1.05 (0.95-1.15)
1.15 (1.05-1.26)
Annual outpatient HIV visits
1-2
1.0 (Ref)
1.0 (Ref)
3-4
0.96 (0.88-1.06)
0.98 (0.89-1.08)
5-6
1.28 (1.16-1.42)
1.27 (1.15-1.42)
≥7
2.53 (2.30-2.78)
2.40 (2.15-2.67)
Insurance†
Private
1.0 (Ref)
1.0 (Ref)
Medicaid
2.39 (2.10-2.71)
1.89 (1.65-2.16)
Medicare/dual eligible
2.40 (2.09-2.75)
1.95 (1.69-2.25)
112
Ryan White/uninsured
1.08 (0.94-1.23)
1.10 (0.95-1.27)
Unknown
0.97 (0.78-1.20)
1.05 (0.83-1.34)
Calendar year
2005
1.0 (Ref)
1.0 (Ref)
2006
1.00 (0.89-1.12)
1.01 (0.90-1.14)
2007
1.02 (0.91-1.15)
1.05 (0.93-1.18)
2008
1.00 (0.89-1.12)
1.05 (0.94-1.18)
2009
0.97 (0.87-1.09)
1.04 (0.92-1.16)
2010
0.99 (0.88-1.11)
0.89 (0.90-1.00)
2011
0.77 (0.69-0.87)
0.89 (0.79-1.00)
Abbreviations: IRR, incidence rate ratio; CI, confidence interval; MSM, men who have
sex with men; IDU, injection drug use; HBV, hepatitis B virus; HCV, hepatitis C virus.
The multivariable model also included an indicator variable for clinical care site. Results
in bold are statistically significant (p≤0.05).
*
Age was assessed on July 1 of each calendar year.
**
HIV risk factors were considered mutually exclusive; subjects who reported IDU in
addition to any other risk factor were categorized as IDU, men who reported sex with
men and women were categorized as MSM.
†
CD4 and insurance data used in this analysis were the first available measurements for
each calendar year.
‡
Hepatitis B and C status were determined by hepatitis B surface antigen and hepatitis C
antibody, respectively, as measured before July 1 of each calendar year. Negative test
results were carried backward to the time of study entry and positive test results were
carried forward for all subsequent years.
113
Table 4-3: Hospitalizations by Diagnostic Category at Nine Sites with ICD-9 Data
Medical
Elite
Low
High
Overall
Control
Control
Viremia
Viremia
n=5,593 (%)
n=1,999 (%)
n=74 (%)
n=1,341 (%)
n=2,179 (%)
Diagnostic Category
Non-AIDS-Defining Infection
1347 (24.1)
394 (19.7)
2 (2.7)
318 (23.7)
633 (29.0)
Cardiovascular
756 (13.5)
335 (16.8)
23 (31.1)
167 (12.4)
231 (10.6)
Gastrointestinal/Liver
521 (9.3)
243 (12.2)
5 (6.8)
107 (8.0)
166 (7.6)
Psychiatric
444 (7.9)
114 (5.7)
7 (9.5)
92 (6.9)
231 (10.6)
Endocrine
346 (6.2)
136 (6.8)
3 (4.0)
98 (7.3)
109 (5.0)
Injury/Poisoning
302 (5.4)
123 (6.2)
2 (2.7)
80 (6.0)
97 (4.4)
Renal
298 (5.3)
129 (6.4)
3 (4.0)
62 (4.6)
104 (4.8)
Pulmonary
267 (4.8)
84 (4.2)
16 (21.6)
66 (4.9)
101 (4.6)
Non-AIDS-Defining Cancer
247 (4.4)
104 (5.2)
3 (4.0)
74 (5.5)
66 (3.0)
Orthopedic
205 (3.7)
85 (4.2)
0 (0)
41 (3.1)
79 (3.6)
Neurologic
194 (3.5)
66 (3.3)
0 (0)
63 (4.7)
65 (3.0)
Symptom-based
154 (2.8)
50 (2.5)
2 (2.7)
35 (2.6)
67 (3.1)
Hematologic
143 (2.6)
41 (2.0)
4 (5.4)
41 (3.1)
57 (2.6)
AIDS-Defining Illness
125 (2.2)
38 (1.9)
2 (2.7)
22 (1.6)
63 (2.9)
Obstetric/Gynecologic
106 (1.9)
18 (0.9)
0 (0)
34 (2.5)
54 (2.5)
Congenital/Perinatal/Unclassified
57 (1.0)
19 (1.0)
1 (1.4)
18 (1.4)
19 (0.9)
Missing
46 (0.8)
13 (0.6)
0 (0)
14 (1.0)
19 (0.9)
Dermatologic
35 (0.6)
7 (0.4)
1 (1.4)
9 (0.7)
18 (0.8)
Diagnostic categories are listed in order of frequency in the overall study population. Results are for the 9 sites with available
hospitalization diagnostic data.
114
Table 4-4: Multivariable Analyses of Factors Associated with Cause-Specific Hospitalization at Nine Sites with ICD-9 Data
Characteristic
HIV control status
Medical control
Elite control
Low viremia
High viremia
Age (years)*
18-34
35-49
50-64
≥65
Race
White
Black
Hispanic
Other/unknown
Gender
Male
Female
HIV risk factor**
Heterosexual
MSM
IDU
Other/unknown
CD4 count (cells/mm3)†
>750
501-750
<500
Hepatitis status‡
HIV mono-infected
All-Cause
Adjusted Incidence Rate Ratio (95% Confidence Interval)
Non-AIDSGastrointestinal
Defining Infection Cardiovascular
/Liver
Psychiatric
Endocrine
1.0 (Ref)
1.99 (1.29-3.06)
1.39 (1.26-1.54)
1.79 (1.61-1.99)
1.0 (Ref)
0.32 (0.08-1.30)
1.57 (1.31-1.88)
2.48 (2.10-2.93)
1.0 (Ref)
3.19 (1.50-6.79)
1.11 (0.87-1.42)
1.18 (0.92-1.51)
1.0 (Ref)
1.32 (0.52-3.33)
0.91 (0.68-1.23)
1.15 (0.85-1.54)
1.0 (Ref)
3.98 (1.54-10.28)
1.65 (1.15-2.37)
3.14 (2.35-4.21)
1.0 (Ref)
1.14 (0.32-4.06)
1.54 (1.14-2.08)
1.42 (1.00-1.99)
1.0 (Ref)
1.10 (0.94-1.28)
1.36 (1.15-1.59)
2.24 (1.77-2.83)
1.0 (Ref)
1.09 (0.87-1.36)
1.18 (0.92-1.51)
1.44 (1.00-2.07)
1.0 (Ref)
2.20 (1.25-3.88)
3.63 (2.05-6.44)
7.86 (4.06-15.20)
1.0 (Ref)
1.23 (0.81-1.87)
1.26 (0.79-2.02)
2.25 (1.25-4.08)
1.0 (Ref)
1.07 (0.64-1.78)
0.72 (0.42-1.26)
0.94 (0.35-2.49)
1.0 (Ref)
1.72 (1.00-2.94)
2.33 (1.38-3.93)
3.95 (2.02-7.72)
1.0 (Ref)
1.05 (0.92-1.20)
0.84 (0.72-0.97)
0.75 (0.52-1.09)
1.0 (Ref)
0.85 (0.69-1.04)
0.71 (0.56-0.88)
0.53 (0.29-0.96)
1.0 (Ref)
1.25 (0.92-1.69)
1.04 (0.75-1.43)
1.08 (0.48-2.46)
1.0 (Ref)
0.94 (0.68-1.30)
0.97 (0.70-1.35)
0.50 (0.17-1.42)
1.0 (Ref)
1.17 (0.80-1.71)
0.79 (0.48-1.30)
0.80 (0.33-1.97)
1.0 (Ref)
1.39 (0.96-2.03)
1.10 (0.66-1.86)
0.51 (0.16-1.67)
1.0 (Ref)
1.23 (1.09-1.39)
1.0 (Ref)
1.19 (0.99-1.42)
1.0 (Ref)
0.98 (0.76-1.26)
1.0 (Ref)
1.09 (0.80-1.48)
1.0 (Ref)
0.93 (0.66-1.33)
1.0 (Ref)
1.30 (0.91-1.86)
1.0 (Ref)
0.83 (0.72-0.96)
1.23 (1.06-1.42)
1.21 (0.96-1.53)
1.0 (Ref)
1.08 (0.88-1.33)
1.56 (1.26-1.92)
1.23 (0.87-1.74)
1.0 (Ref)
0.62 (0.46-0.85)
0.71 (0.51-0.97)
0.74 (0.46-1.20)
1.0 (Ref)
0.92 (0.64-1.30)
1.47 (1.03-2.10)
1.20 (0.71-2.04)
1.0 (Ref)
0.96 (0.63-1.46)
2.15 (1.44-3.33)
1.58 (0.61-4.08)
1.0 (Ref)
0.73 (0.46-1.14)
1.21 (0.77-1.89)
1.79 (0.93-3.48)
1.0 (Ref)
1.11 (0.99-1.23)
1.28 (1.15-1.43)
1.0 (Ref)
1.08 (0.90-1.28)
1.28 (1.08-1.52)
1.0 (Ref)
0.89 (0.70-1.13)
1.00 (0.78-1.30)
1.0 (Ref)
1.11 (0.83-1.50)
1.17 (0.86-1.58)
1.0 (Ref)
1.32 (0.95-1.84)
1.16 (0.82-1.63)
1.0 (Ref)
1.18 (0.82-1.71)
1.18 (0.83-1.69)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
115
HIV/HBV co-infected
1.07 (0.78-1.46)
1.21 (0.87-1.69)
0.72 (0.32-1.63)
1.03 (0.54-1.99)
0.87 (0.38-1.98)
0.94 (0.34-2.58)
HIV/HCV co-infected
1.33 (0.94-1.90)
1.29 (0.85-1.96)
1.40 (0.90-2.16)
1.36 (1.17-1.57)
1.33 (1.07-1.66)
1.53 (1.11-2.10)
HIV/HBV/HCV tri-infected 1.40 (0.95-2.08)
1.18 (0.78-1.80)
1.51 (0.80-2.86)
0.25 (0.04-1.74)
0.80 (0.32-2.01)
2.41 (1.17-4.96)
Unknown
1.06 (0.94-1.20)
0.94 (0.78-1.13)
1.27 (0.98-1.65)
1.13 (0.84-1.52)
0.73 (0.48-1.09)
1.48 (1.02-2.17)
HIV care (visits/year)
1-2
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
3-4
0.93 (0.82-1.06)
0.81 (0.65-1.01)
0.87 (0.58-1.30)
1.04 (0.73-1.48)
0.77 (0.54-1.09)
1.06 (0.70-1.60)
5-6
1.24 (0.83-1.84)
0.97 (0.65-1.44)
1.30 (1.13-1.48)
1.27 (1.01-1.59)
1.52 (1.03-2.24)
1.96 (1.24-3.11)
≥7
2.68 (2.34-3.07)
2.64 (2.11-3.31)
3.04 (2.02-4.57)
3.88 (2.76-5.46)
2.01 (1.43-2.82)
3.53 (2.32-5.36)
Insurance†
Private
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
Medicaid
1.21 (0.82-1.78)
1.57 (0.91-2.72)
1.72 (1.47-2.02)
1.91 (1.47-2.47)
2.15 (1.42-3.27)
2.47 (1.55-3.93)
Medicare/dual eligible
1.29 (0.86-1.92)
1.81 (1.53-2.15)
2.08 (1.59-2.72)
2.08 (1.38-3.13)
2.78 (1.72-4.48)
2.45 (1.47-4.08)
Ryan White/uninsured
1.08 (0.91-1.28)
1.26 (0.96-1.67)
1.44 (0.94-2.21)
0.70 (0.45-1.09)
1.14 (0.65-2.02)
1.94 (1.21-3.10)
Unknown
0.99 (0.75-1.30)
1.05 (0.64-1.72)
0.56 (0.22-1.45)
1.51 (0.66-3.48)
0.43 (0.08-2.26)
1.98 (1.07-3.67)
Calendar year
2005
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
1.0 (Ref)
2006
0.95 (0.81-1.10)
0.85 (0.66-1.10)
1.43 (0.97-2.12)
0.94 (0.58-1.52)
1.33 (0.75-2.38)
0.49 (0.32-0.76)
2007
1.10 (0.95-1.27)
1.01 (0.78-1.31)
1.41 (0.97-2.04)
1.06 (0.67-1.69)
0.68 (0.43-1.08)
1.68 (1.02-2.74)
2008
1.04 (0.90-1.20)
0.94 (0.74-1.20)
1.26 (0.87-1.84)
1.18 (0.75-1.86)
0.74 (0.49-1.12)
1.42 (0.83-2.44)
2009
1.04 (0.90-1.19)
0.92 (0.72-1.16)
1.13 (0.78-1.65)
1.49 (0.97-2.28)
0.42 (0.28-0.64)
1.82 (1.02-3.23)
2010
1.01 (0.88-1.16)
0.88 (0.69-1.13)
1.33 (0.92-1.93)
1.42 (0.93-2.15)
0.74 (0.51-1.10)
1.47 (0.87-2.47)
2011
0.96 (0.84-1.11)
0.99 (0.78-1.26)
0.88 (0.60-1.27)
1.30 (0.85-1.99)
1.55 (0.92-2.62)
0.42 (0.26-0.67)
Abbreviations: IRR, incidence rate ratio; CI, confidence interval; MSM, men who have sex with men; IDU, injection drug use; HBV, hepatitis B
virus; HCV, hepatitis C virus. Models also included an indicator variable for clinical care site. Results in bold are statistically
significant (p≤0.05).
*
Age was assessed on July 1 of each calendar year.
**
HIV risk factors were considered mutually exclusive; subjects who reported IDU in addition to any other risk factor were
categorized as IDU, men who reported sex with men and women were categorized as MSM
†
CD4 and insurance data used in these analyses were the first available measurements for each calendar year.
‡
Hepatitis B and C status were determined by hepatitis B surface antigen and hepatitis C antibody, respectively, as measured before
July 1 of each calendar year. Negative test results were carried backward to the time of study entry and positive test results were
carried forward for all subsequent years
116
Figure 4-1: Unadjusted All-Cause Hospitalization Rates by HIV Control Status
Rates were calculated using total number of visits as the numerator and aggregate persontime as the denominator for each calendar year and standardized as hospitalizations per
100 person-years of follow-up.
117
CHAPTER 5:
CONCLUSION
118
HIV infection has evolved from a rapidly fatal condition to a chronic and
manageable one. The medications, laboratory tests and healthcare services that enabled
this transformation have not come cheaply. Federal and state governments provide lifesaving funding for healthcare to PLWH through Medicare, Medicaid, and the Ryan White
CARE programs. The fact that the life expectancy among newly-diagnosed PLWH is
now approaching that of the general population is a testament to the success of these
efforts.1,2 However, these programs have required emergency allocations of federal funds
every year since 2010 in order to meet the growing demand for medications and
healthcare services.3 For multiple reasons, this demand can only be expected to increase
in the coming years. First, the ACA will expand healthcare coverage to include new
individuals, thereby linking previously uninsured PLWH to healthcare services. Second,
the ACA is likely to increase screening for HIV infection, thereby identifying PLWH
currently unaware of their diagnosis and linking them into care for this condition. Third,
the population of PLWH already linked to care is aging and has an increasing burden of
age-related comorbidities such as cardiovascular disease and malignancies that contribute
to healthcare costs. It is imperative that strategies be developed to attenuate the rising
costs of healthcare among PLWH in order to maintain the solvency of publicly-funded
healthcare programs. Doing so requires an understanding of the factors influencing
healthcare utilization among PLWH. The research presented in this dissertation provides
insight into the roles played by hepatitis co-infection and elite control. Knowledge of how
these factors influence healthcare utilization can help inform the allocation of healthcare
resources, improve the cost-effectiveness of HIV care, and guide clinical decisionmaking.
119
The studies presented in Chapters 2 and 3 each demonstrate that HBV and HCV
are important drivers of hospitalizations among PLWH. Because hospitalizations
represent a substantial component of total healthcare costs among PLWH, healthcare
systems and providers that are caring for PLWH who are co-infected with HBV and/or
HCV must be prepared to handle higher healthcare costs than those incurred by persons
with HIV mono-infection.4,5 Public funders may consider diverting scarce healthcare
resources toward this more vulnerable patient population and managed care organizations
may consider increasing capitation rates to providers caring for PLWH who are coinfected with viral hepatitis. Furthermore, research is needed to identify specific
interventions that may attenuate the risk of hospitalization among PLWH who are coinfected with viral hepatitis.
The study presented in Chapter 2 provides some insight into potentially
modifiable factors that contribute to the excess risk of hospitalization among PLWH with
hepatitis co-infection. In this study, hospital admission for non-AIDS-defining infection
was more common among PLWH with any hepatitis co-infection. Among such infections,
pneumonia was most common. Routine pneumococcal and influenza vaccinations have
been shown to decrease hospitalizations and are recommended for all PLWH in the
United States.6-9 These recommendations may be particularly important for PLWH who
are co-infected with viral hepatitis.
Psychiatric hospitalizations were disproportionately common among HIV/HCV
co-infected patients, with the most common admitting diagnosis being depression. Mood
disorders and depression are common among PLWH, with studies reporting prevalence
of 18-81% depending on the specific population and diagnostic methodology used.10
120
HIV/HCV co-infection is associated with higher prevalence and severity of
neuropsychiatric disease than is HIV infection alone.11,12 The successful management of
mental health disorders among PLWH, and specifically those with HIV/HCV coinfection, can lead to improved quality of life, improved adherence to medication, and
reduction of high-risk behaviors and suicidality.13-16 One effective strategy to manage
mental health disorders among this population includes integration of mental health and
HIV care programs, which has been shown to improve clinical outcomes of HIV and
psychiatric disease as well as decrease hospitalization costs.17,18 HIV/HCV co-infected
patients may benefit from targeted interventions to improve mental health, including colocation of services to care for HIV, hepatitis, and psychiatric disorders.
The study presented in Chapter 3 reinforces the importance of psychiatric disease
as a driver of healthcare utilization among HIV/HCV co-infected patients by
demonstrating their increased utilization of outpatient mental health services as compared
to patients with HIV mono-infection. Furthermore, non-White PLWH were significantly
less likely to utilize mental health services than were those reporting White race/ethnicity,
potentially reflecting cultural barriers to care or other access issues that may require
targeted intervention.19 Healthcare delivery systems caring for PLWH, and specifically
those with HIV/HCV co-infection, must proactively address mental health issues in order
to improve clinical outcomes and ultimately reduce total healthcare costs. It is unclear
why utilization of mental health care services appears to be decreasing among both HIV
mono-infected and HIV/HCV co-infected patients during the study interval from 2006 to
2011. This trend raises concern that funding limitations or other barriers may be limiting
access to an important healthcare service in a population with particular need for mental
121
health care. Providing access to mental health services should be a priority for all
healthcare systems caring for PLWH and is especially important in the setting of
HIV/HCV co-infection.
The study presented in Chapter 3 also demonstrated that utilization of outpatient
primary HIV care services is decreasing over time and is not influenced by hepatitis
serostatus. This decline is almost certainly due to updated professional society guidelines
that recommend less frequent monitoring for patients with well-controlled HIV disease.2022
This observation may help to predict future needs for outpatient primary HIV care
services, but the decline in number of visits per patient must also be balanced with the
expectation that a greater number of patients will require services as a result of insurance
and HIV screening changes brought about by the ACA.
The studies in Chapters 2 and 3 both raise questions about the potential role of
therapies directed against HBV and HCV in decreasing healthcare utilization and, ideally,
healthcare costs. Such therapies have been shown to decrease progression to cirrhosis
among co-infected PLWH.23 In Chapter 3, the observed association between higher FIB4 score and increased hospitalization rates suggests that hepatocellular dysfunction may
directly contribute to the risk of hospitalization in co-infected patients. It might therefore
be inferred that reducing hepatic injury by treating the underlying viral hepatitis may
reduce the risk of hospitalization. However, in this study, use of ART with activity
against HBV by persons with HIV/HBV co-infection only modestly reduced the rate of
primary HIV care visits and was associated with a non-significant trend towards fewer
hospitalizations. Treatment of HBV is common among PLWH and over 70% of the
HIV/HBV co-infected patients evaluated in Chapter 3 were prescribed such therapies.
122
Conversely, prior studies have reported HCV treatment rates of only 20-40% for
HIV/HCV co-infected patients, with less than half of these achieving sustained virologic
response.24,25 More effective and better tolerated therapies for HCV have recently been
developed and could potentially transform HCV into a much more readily cured
disease.26-28 If these therapies decrease hospitalization rates, the associated cost savings
could counterbalance the high cost of the newest medications used to treat HCV.29,30
Further research on this topic is needed. Interestingly, one study evaluating the treatment
of HIV/HCV co-infected patients with triple therapy against HCV did not show a
resultant decline in the incidence of major depressive disorder, which we have already
described as an important co-morbidity among persons with HIV/HCV co-infection.31
The study presented in Chapter 4 demonstrated that hospitalization rates were
higher among PLWH with elite control of HIV as compared to persons whose virus was
medically controlled with ART. Because elite control is a rare phenomenon, representing
less than 1% of PLWH, excess hospitalizations among this group do not represent a large
contributor to the total healthcare costs associated with HIV infection.32 The more
important inference that may be drawn from the data presented is that the clinical
outcomes of elite control may be worse than the outcomes associated with successful
virologic control using ART. This is important for at least two reasons. First, individual
elite controllers may experience a benefit from initiation of ART. Second, elite control
may be a suboptimal model for the functional cure of HIV.
The notion that elite controllers may benefit from ART initiation is supported by a
growing body of evidence. It has previously been shown that elite control is associated
with chronic immune activation and low-grade inflammation beyond the levels seen
123
among PLWH who are medically controlled with ART.33-35 This may place elite
controllers at higher risk of clinical events that are sequelae of chronic immune activation
and inflammation, such as cardiovascular events. Prior studies have demonstrated a
higher burden of coronary atherosclerosis among elite controllers than among medically
controlled PLWH and HIV-uninfected comparators.36,37 Consistent with this observation,
the data presented in Chapter 4 demonstrate that cardiovascular disease was the most
common reason for hospitalization among elite controllers, accounting for about onethird of all hospitalizations. Risk of hospitalization for cardiovascular disease remained
elevated among elite controllers as compared to those with medically controlled HIV
after adjusting for other clinical factors, although data on known cardiovascular risk
factors such as dyslipidemia and hypertension were not included in the statistical models.
If cardiovascular events among elite controllers occur as a result of chronic inflammation
and immune activation, then it is possible that elite controllers could benefit from
interventions to reduce this inflammation and immune activation. ART has been shown
to reduce low-level viral replication measured by ultrasensitive assays among
spontaneous controllers of HIV and also reduces markers of T cell activation in blood and
the gut of these patients.38 Adjunctive therapy with anti-inflammatory agents such as nonsteroidal anti-inflammatory drugs and HMG-CoA reductase inhibitors should also be
investigated.
Elite control has often been looked to as a model for HIV remission, given the
presumed value of viral suppression without lifelong ART. While ART has successfully
transformed HIV into a chronic disease, its use is complicated by toxicities, the need for
lifelong adherence and the constant threat of emerging drug-resistance.39-41 Even among
124
PLWH who are successfully treated with ART, some immunologic dysfunction
persists.42-45 Therefore, it remains desirable to develop curative approaches that confer
durable virologic control, delay disease progression, restore immunologic function and
decrease morbidity without a requirement for perpetual ART. Evidence suggests that elite
control does not meet these objectives and is inferior to ART. Viral replication occurs
among elite controllers at levels that are often higher than those seen among PLWH who
are well-controlled on ART.46,47 Despite virologic control, some elite controllers still
experience CD4 decline and progression to AIDS.35 Elite controllers have evidence of
immune dysfunction and inflammation that is higher than that seen among PLWH who
are well-controlled on ART.35,48,49 Now, the data presented in Chapter 4, indicate that
clinical outcomes represented by the need for hospitalization are worse among elite
controllers than among PLWH who are well-controlled on ART. This suggests that
interventions designed to induce a state similar to elite control may not be as desirable as
other strategies to achieve HIV remission that may more closely reproduce or exceed the
beneficial effects of ART.
The research presented in this dissertation provides valuable insight into factors
contributing to healthcare utilization and healthcare costs. It clearly demonstrates that
hepatitis co-infection is an important driver of healthcare utilization. Interventions to
decrease the rate of costly hospitalizations in co-infected patients must be investigated
and implements. Known strategies to prevent infectious causes of hospitalization, such as
pneumococcal and influenza vaccinations, must be recognized by providers and
prioritized by third-party payors. Similarly, evidence-based interventions to improve the
mental health of PLWH, particularly those with HIV/HCV co-infection, must be
125
implemented in order to improve clinical outcomes and reduce total healthcare costs.
Third-party payors should be aware of the trends in healthcare utilization over time as
they plan for future healthcare needs. As the HIV-infected population ages, chronic
hepatitis co-infection is likely to play an even greater role in the long-term morbidity of
PLWH. Clinicians must be prepared to address the needs associated with the long-term
care of co-infected individuals. Clinicians must also recognize the potential benefit of
ART use among elite controllers. Further investigation is needed to prospectively
evaluate the influence of ART use and anti-inflammatory agents among elite controllers.
Tremendous strides in the management of HIV have been achieved over the last 30 years
and additional successes may soon be seen in linking a greater number of PLWH to lifesaving medical care. With an understanding of the factors contributing to healthcare costs,
policy-makers and third-party payors may be able to design systems to achieve more
cost-effective care for PLWH. With an understanding of the clinical outcomes and
underlying physiology involved in the spontaneous control of HIV, we may someday
identify an intervention to achieve HIV remission.
126
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130
CURRICULUM VITAE
DEMOGRAPHIC INFORMATION
Trevor Adam Crowell, M.D.
Born: December 8, 1982, Sacramento, California
Current Appointments
2014-present Research Physician, U.S. Military HIV Research Program
2014-present Assistant Professor of Medicine, Division of Infectious Diseases,
Uniformed Services University
2014-present Adjunct Assistant Professor of Medicine, Division of Infectious Diseases,
Johns Hopkins Hospital University
Contact Data
Division of Infectious Diseases
Department of Internal Medicine
1830 East Monument St., Suite 450
Baltimore, MD 21287
301-500-3990
301-500-3666
Trevor.crowell@jhmi.edu
Education and Training
Undergraduate
2004
B.A., Biology and History, Rice University, Houston, TX
Doctoral
2008
Candidate
Postdoctoral
2008-2011
2011-2014
M.D., Medicine, Keck School of Medicine of the University of Southern
California, Los Angeles, CA
PhD, Clinical Investigation, Johns Hopkins Bloomberg School of Public
Health, Baltimore, MD
Residency, Internal Medicine and International Health, University
Hospitals Case Medical Center, Cleveland, OH
Fellowship, Infectious Diseases, Johns Hopkins Hospital, Baltimore, MD
Professional Experience
2014-present Research Physician, U.S. Military HIV Research Program, Silver Spring,
MD
131
RESEARCH ACTIVITIES
Peer Reviewed Original Science Publications
1. Crowell TA, Gebo KA, Balagopal A, Fleishman JA, Agwu AL, Berry SA.
“Impact of Hepatitis Coinfection on Hospitalization Rates and Causes in a
Multicenter Cohort of Persons Living with HIV.” JAIDS: Journal of Acquired
Immune Deficiency Syndromes. 2014 Apr 1;65(4):429-37.
2. Crowell TA, Berry SA, Fleishman JA, LaRue RW, Korthuis PT, Nijhawan AE,
Moore RD, Gebo KA. “Impact of Hepatitis Co-Infection on Healthcare
Utilization among Persons Living with HIV.” JAIDS: Journal of Acquired
Immune Deficiency Syndromes. 2015 Apr 1;68(4):425-31.
3. Ananworanich J, Sirivichayakul S, Pinyakorn S, Crowell TA, Trichavaroj R,
Weerayingyong J, Chomchey N, Fletcher JLK, van Griensven F, Phanuphak P,
Robb ML, Michael NL, Kim JH, Phanuphak N. “High Prevalence of Transmitted
Drug Resistance in Acute HIV-Infected Thai Men Who Have Sex with Men.”
JAIDS: Journal of Acquired Immune Deficiency Syndromes. 2015 Apr
1;68(4):481-5.
4. Crowell TA, Gebo KA, Blankson JN, Korthuis PT, Yehia BR, Rutstein RM,
Moore RD, Sharp V, Nijhawan AE, Mathews WC, Hanau LH, Corales RB, Beil
R, Somboonwit C, Edelstein H, Allen SL, Berry SA. “Elite Controllers are
Hospitalized More Often than Persons with Medically Controlled HIV.” The
Journal of Infectious Diseases. Advance access published 2014 Dec 15, doi:
10.1093/infdis/jiu809.
Invited Reviews
1. Crowell TA, Hatano H. “Clinical Outcomes and Antiretroviral Therapy in “Elite”
Controllers: A Review of the Literature.” Journal of Virus Eradication (in press).
Conference Presentations
1. Bohnsack BL, Lai L, Patniyot I, Crowell TA, Northrop JL, Justice MJ, Hirschi
KK. Quaking regulation of visceral endoderm function is required for vascular
remodeling and retinoic acid-mediated control of endothelial cell proliferation.
[Poster] 14th International Vascular Biology Meeting. Toronto, ON. 1-5 June
2004.
2. Crowell TA. Humanitarian Medical Outreach Projects in Bungoma, Kenya.
[Poster] 14th Annual International Health Medical Education Consortium
Conference. San Francisco, CA. 30 March-1 April 2005.
3. Crowell TA. Humanitarian Medical Outreach Projects in Bungoma, Kenya.
[Poster] 7th Annual Bay Area International Health Conference. San Francisco,
CA. 2 April 2005.
4. Dando S, Crowell TA, Lederman MM, Rodriguez B. Functional Immune
Response is Associated with Risk of Cancer in a Cohort of HIV Infected
Patients. [Poster] 17th Conference on Retroviruses and Opportunistic
Infections. San Francisco, CA. 16-19 February 2010.
132
5. Finn M, Crowell TA. A Rapidly Disseminating Rash in an HIV Positive
Woman: Is It Really Just Another Case of VZV? [Oral] American College of
Physicians Maryland Chapter Associates Meeting. Baltimore, MD. 10 May
2012.
6. Crowell TA, Gebo KA, Balagopal A, Berry SA. Impact of Hepatitis Serostatus
on Hospitalization Rates and Reasons for Admission in a Multi-Center Cohort
of Persons Living with HIV in the U.S. [Poster] 20th Conference on
Retroviruses and Opportunistic Infections. Atlanta, GA. 3-6 March 2013.
7. Crowell TA, Lam JO, Ugarte-Gil C, Paik J, Drummond MB, Lambert AA.
Outpatient Proton Pump Inhibitor Therapy and Risk of Community-Acquired
Pneumonia: A Systematic Review and Meta-Analysis. [Poster] IDWeek 2013.
San Francisco, CA. 2-6 October 2013.
8. Crowell TA, Berry SA, Gebo KA. Impact of Hepatitis Serostatus on Primary
Care, Mental Health, Emergency Room and Inpatient Utilization in Persons
Living with HIV. [Oral] IDWeek 2013. San Francisco, CA. 5 October 2013.
9. Crowell TA, Gebo KA, Blankson JN, Yehia BR, Rutstein RM, Berry SA. HIV
Elite Controllers are Hospitalized More Often than Persons with Medically
Controlled HIV. [Poster] 21st Conference on Retroviruses and Opportunistic
Infections. Boston, MA. 3-6 March 2014.
10. Crowell TA, Fletcher JLK, Kroon E, Pinyakorn S, Schuetz A, Sereti I, Krebs
SJ, Slike BM, Chomont N, Jagodzinski LL, Sandler N, Dewar R, Rerknimitr
R, Rattanamanee S, Trichavaroj R, Valcour VG, Spudich S, Robb ML, Kim
JH, Michael NL, Phanuphak N, Ananworanich J. Acute Retroviral Syndrome
is Associated with Gut Mucosal CD4 Depletion, Inflammation and High Viral
and Proviral Burden in Systemic and Tissue Compartments. [Oral] IDWeek
2014. Philadelphia, PA. 10 October 2014.
11. Fletcher JLK, Crowell TA, Dewar R, Sereti I, Slike BM, Rerknimitr R,
Michael NL, Chomont N, Ananworanich J. “HIV Burden and Biomarker
Associations with Colonic HIV RNA during Acute HIV Infection.” [Oral] 22nd
Conference on Retroviruses and Opportunistic Infections. Seattle, WA. 24
February 2015.
Extramural Sponsorship
Previous
Title:
Number:
Sponsor:
Role:
Dates:
Research Training in Microbial Diseases
T32 AI007291
NIH/NIAID
Research Fellow/Trainee
7/2012 – 3/2014
133
EDUCATIONAL ACTIVITIES
Educational Publications
Book Chapters, Monographs
1. Crowell TA. “Helicobacter pylori.” In Loue S, Sajatovic M (Eds.) Encyclopedia
of Immigrant Health. New York: Springer, November 2011, pp 813-815.
2. Crowell TA. “Autoimmune Pathology.” In Le T, Krause K (Eds.) First Aid for
the Basic Sciences: General Principles. New York: McGraw-Hill, December
2008, pp 490-501.
3. Crowell TA. “Microbiology: Systems.” In Le T, Krause K (Eds.) First Aid for the
Basic Sciences: General Principles. New York: McGraw-Hill, December 2008, pp
408-424.
Other media
1. Ghanem KG, Crowell TA. “Enterobius (Pinworm).” In Bartlett JG, Auwaerter
PG, Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkinsabxguide.org
2. Ghanem KG, Crowell TA. “Hookworm.” In Bartlett JG, Auwaerter PG, Pham P,
Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkins-abxguide.org
3. Ghanem KG, Crowell TA. “Prion Diseases.” In Bartlett JG, Auwaerter PG, Pham
P, Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkins-abxguide.org
4. Ghanem KG, Crowell TA. “Shigella dysenteriae.” In Bartlett JG, Auwaerter PG,
Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkins-abxguide.org
5. Ghanem KG, Crowell TA. “Streptobacillus moniliformis.” In Bartlett JG,
Auwaerter PG, Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide.
www.hopkins-abxguide.org
6. Ghanem KG, Crowell TA. “Yersinia species (non-plague).” In Bartlett JG,
Auwaerter PG, Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide.
www.hopkins-abxguide.org
7. Ghanem KG, Crowell TA. “Taenia saginata.” In Bartlett JG, Auwaerter PG,
Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkins-abxguide.org
8. Ghanem KG, Crowell TA. “Trichinella species.” In Bartlett JG, Auwaerter PG,
Pham P, Hsu AJ (Eds.) Johns Hopkins ABX Guide. www.hopkins-abxguide.org
Teaching
Classroom instruction
2001-2004
Fundamentals of Experimental Biology, teaching assistant for 24
undergraduates, 4 hours/session, 64 sessions/year, Rice University
2002-2004
Introductory Biology, teaching assistant for 160 undergraduates, 2
hours/session, 32 sessions/year, Rice University
2003-2004
Basic Life Support, instructor for 30 community participants, 4
hours/session, 4 sessions/year, Rice Emergency Medical Services
2003-2004
Advanced Experimental Biosciences, teaching assistant for 24
undergraduates, 5 hours/session, 16 sessions/year, Rice University
134
2005-2006
2005-2006
2009-2011
2009-2011
2009-2011
2010-2011
Law School Admission Test, tutor for 1 senior undergraduate, 2
hours/session, 52 sessions/year, Kaplan Test Prep & Admissions
Medical College Admission Test, instructor for 30 senior undergraduates,
3 hours/session, 26 sessions/year, Kaplan Test Prep & Admissions
Physical Diagnosis 1, instructor for 50 first-year medical students, 2
hours/session, 7 sessions/year, Case Western Reserve University School of
Medicine
Physical Diagnosis 2, small group leader for 6 second-year medical
students, 4 hours/session, 6 sessions/year, Case Western Reserve
University School of Medicine
IQ+, small group leader for 10 third-year medical students, 4
hours/session, 18 sessions/year, Case Western Reserve University School
of Medicine
Physical Diagnosis 3, small group leader for 4 third-year medical students,
4 hours/session, 6 sessions/year, Case Western Reserve University School
of Medicine
Clinical Instruction
2011-2012
Polk Inpatient HIV Service, Fellow, 10 weeks/year, Johns Hopkins
Hospital
2014-present Infectious Diseases Inpatient Consultation Service, Attending, 4-6
weeks/year, Bayview Medical Center
2015-present Internal Medicine Service, Attending, 2-4 weeks/year, Walter Reed
National Military Medical Center
CME Instruction
2011-2013
Infectious Diseases for the Primary Care Provider, Lecturer, “Clinical
Cases,” Johns Hopkins
CLINICAL ACTIVITIES
Certification
Medical, other state/government licensure
2008-2011
State Medical Board of Ohio, Training License #57.014744
2011-present Maryland Board of Physicians, License #D71820
Boards, other specialty certification
2011-present American Board of Internal Medicine, Internal Medicine
2013-present American Board of Internal Medicine, Infectious Diseases
Clinical (Service) Responsibilities
2014-present Infectious Diseases Inpatient Consultation Service, Attending, 4-6
weeks/year, Bayview Medical Center
2015-present Internal Medicine Service, Attending, 2-4 weeks/year, Walter Reed
National Military Medical Center
135
ORGANIZATIONAL ACTIVITIES
Editorial Activities
Journal Reviewer
2012-present Sexually Transmitted Infections
2014-present AIDS Research and Therapy
2014-present World Journal of Gastroenterology
2015-present Journal of the International AIDS Society
2015-present Clinical Infectious Diseases
Professional Societies
2004-present Global Health Education Consortium, Member
2011-present HIV Medicine Association, Member
2011-present Infectious Diseases Society of America, Member
RECOGNITION
Awards, Honors
2000
Cum Laude Society Inductee, Phillips Exeter Academy Cum Laude
Society
2002
Rotary Community Service Scholarship, Rotary Club of West University
Place
2002
Dr. Bill Wilson Student Initiative Grant, Rice University
2003
All-Around Scholarship, Target Corporation
2004
Outstanding Senior Award, Rice University Student Association
2005
Medical Educators’ Collegium Grant, University of Southern California
2005-2007
Scholarship for Commitment to Internal Medicine, MacKenzie
Foundation
2006-2008
Community Service Scholarship, Medical Faculty Wives and Friends
2007
Keck Service Spirit Scholarship, University of Southern California
2007
Celebrate Life Scholarship, USC/University Hospital Guild
2008
Order of Areté Student Recognition Award, University of Southern
California
2008
Dr. George S. Herron Memorial Award, University of Southern
California
2010
Resident Research Award, Case Western Reserve University
Department of Medicine
2011
Resident Teaching Award, Case Western Reserve University
Department of Medicine
2013
Program Committee Choice Award, IDWeek
2014
Young Investigator Award, Conference on Retroviruses and
Opportunistic Infections
2014
Trainee Travel Grant, IDWeek
136