HIV Practice Survey

HIV Clinician Workforce Study
Final Design Report
March 31, 2011
Mathematica Policy Research
Ellen Bouchery
Boyd Gilman
Julie Ingels
Margaret Hargreaves
Cicely Thomas
The Lewin Group
Paul Hogan
Namrata Sen
Rita Furst-Seifert
Rod Hooker

Contract Number:
GS10F0050L/HHSH250201000122G
Mathematica Reference Number:
06869
Submitted to:
HIV/AIDS Bureau
Health Resources and Services Administration
Parklawn Building, Suite 7-05
5600 Fisher’s Lane
Rockville, MD 20857
Task Order Officer: Sylvia Trent-Adams, PhD,
MS, RN
Submitted by:
Mathematica Policy Research
955 Massachusetts Avenue
Suite 801
Cambridge, MA 02139
Telephone: (617) 491-7900
Facsimile: (617) 491-8044
Project Director: Boyd Gilman

HIV Clinician Workforce Study
Final Design Report
March 31, 2011
Mathematica Policy Research
Ellen Bouchery
Boyd Gilman
Julie Ingels
Margaret Hargreaves
Cicely Thomas
Positive Outcomes, Inc.
Paul Hogan
Namrata Sen
Rita Furst-Seifert
Rod Hooker

www.mathematica-mpr.com

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Mathematica® is a registered trademark of Mathematica Policy Research

Contents

Mathematica Policy Research/The Lewin Group

CONTENTS
I

INTRODUCTION ............................................................................................. 1
A. Objectives of the Model ........................................................................... 1
B.

Overview of Approach ............................................................................. 1
1.
2.

II

Primary Data and Information Sources .............................................. 1
Components of the Model ................................................................ 2

MODEL DESIGN .............................................................................................. 5
A. Baseline Estimates of Supply and Demand ............................................... 5
1.
2.
B.

Baseline Supply of HIV Clinicians ...................................................... 5
Baseline Demand for HIV Clinicians................................................. 11

Estimating Excess Demand .................................................................... 13
1.
2.

Needs-Based Estimate of Demand .................................................. 14
Market-Based Estimate of Demand ................................................. 15

C. Projecting Supply and Demand .............................................................. 16
1.
2.

Projecting Supply of HIV Clinicians.................................................. 16
Projecting Demand for HIV Clinicians .............................................. 22

REFERENCES................................................................................................. 25
APPENDIX A: METHODOLOGY FOR IDENTIFYING HIV CLINICIANS
FROM CLAIMS DATA .................................................................................... 27
APPENDIX B: ICD-9-CM AND CPT CODES FOR IDENTIFYING TREATED
PATIENTS WITH HIV INFECTION .................................................................... 31
APPENDIX C: NATIONAL DRUG CODES FOR IDENTIFYING TREATED
PATIENTS WITH HIV INFECTION .................................................................... 35

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Tables

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TABLES
II.1

Number of FTE Clinicians in Total and HIV Care ...................................... 9

II.2

Open Vacancies for Funded HIV Clinicians and Length of Time
to Fill Position ...................................................................................... 16

II.3

Summary of Supply-Side Factors .......................................................... 21

II.4

Summary of Demand-Side Factors ........................................................ 24

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Figures

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FIGURES
II.1

Approach to Estimating FTE Physician Supply ....................................... 11

II.2

Approach to Projecting Total FTE Supply of HIV Clinicians .................... 22

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I.

INTRODUCTION

The purpose of this report is to provide a comprehensive description of our methodology for
developing, estimating, and projecting the HIV clinician supply and demand model. We have
tailored the approach to the specific characteristics of the HIV clinician workforce, and have
incorporated comments made by participants at the expert consultation meeting held on February
23-24 in Washington, DC. In the next section, we outline the objectives of the model. Then we
provide an overview of the primary data and information sources that will be used to develop the
model and the basic components of the model.
A. Objectives of the Model
The objective of the study is to provide the HIV/AIDS Bureau (HAB) in the Health Resources
and Services Administration (HRSA) with national- and regional-level estimates of the number of
clinicians providing HIV-related medical care and projections of the magnitude of expected HIV
clinician shortages or surpluses in the future.
The primary research questions related to the HIV clinician supply and demand model are as
follows:
• How many clinicians currently provide HIV-related medical care in Ryan White- and
non-Ryan White-funded settings and what are their characteristics?
• What is the current market demand and need for HIV-related clinicians? What will be
the market demand and need for HIV-related clinicians in the future?
• What specific factors will influence the market demand and need for HIV-related
clinicians in the future?
• What specific factors will influence the supply of HIV-related clinicians in the future?
• Will the projected supply of HIV-related clinicians in the future be sufficient to meet the
demand and need for HIV-related care?
• How does HIV workforce capacity vary by type of clinician, practice setting, and
geographic region?
We designed the HIV clinician supply and demand model to address these research questions.
We model baseline and projected supply and demand by geographic location so that regional
variation in workforce capacity issues can be addressed.
B. Overview of Approach
In this section we provide an overview of our approach to the model. First, we highlight the
primary data and information sources that will provide the basis for our estimates. Then we provide
a brief overview of our approach to developing estimates for each component of the model.
1.

Primary Data and Information Sources

In this section we provide an overview of the primary data and information sources that will
provide the basis for the model. The primary data sources include:
• Expert Consultation Meeting. The project team convened and facilitated an expert
consultation meeting on February 23-24, 2011. The participating experts and staff from
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HRSA reviewed the proposed model design and provided feedback. We revised the
model design based on this feedback.
• Clinical Consultants. The project team includes several clinical consultants with
experience providing HIV medical care in a range of settings and geographic locations.
On an ad hoc basis, the project team will consult with these individuals to obtain
information on clinical topics.
• National HIV Clinic and Clinician Workforce Surveys. We will conduct two
nationally representative surveys, one of HIV clinicians who bill independently for their
services and the other of the clinics in which they practice as part of this study. The
clinician (individual-level) survey will include questions on provider demographic and
professional characteristics, hours worked in total and HIV patient care, practice setting
characteristics, and strategies for increasing HIV clinician workforce capacity. The clinic
(organization-level) survey will include questions on facility characteristics, workforce
capacity characteristics, organizational characteristics, patient characteristics, staffing and
patient management practices, and output measures.
• Claims Data. We will use ambulatory medical and pharmacy claims data representing
Medicare and Medicaid fee-for-service beneficiaries and the commercially insured
population to identify physicians, nurse practitioners, and physician assistants who bill
for services and provide a minimum level of HIV care. We will also use these data to
understand utilization and practice patterns to support our model assumptions.
• Centers for Disease Control and Prevention (CDC) Surveillance System. The CDC
surveillance system provides counts of the number of individuals living with HIV and
AIDS by demographic and clinical characteristics including age, gender, geographic
location, and AIDS diagnosis. They also provide estimates of undiagnosed cases. These
data will form the basis of our demand assumptions.
• Other Existing Data Sources. We will use other existing sources of utilization and
provider characteristics data to support the study. In particular, the National Center for
Health Statistics (NCHS) health care utilization surveys will help inform the demand
analysis, and data from the Surveys of Physicians Over/Under 50 conducted jointly by the
American Association of Medical College (AAMC) and the American Medical
Association (AMA) will provide information on physician retirement and hours worked
by age and gender. In addition, we will supplement our counts of HIV providers with
membership and certification data from the HIV Medicine Association (HIVMA) and
American Academy of HIV Medicine (AAHIVM), as well with lists of attendees at the
2010 national HIV/AIDS Clinical Conference and participations in regional AIDS
Education and Training Center (AETC) training programs.
In the next section, we provide information on how these data and information sources will be
used to support the model.
2.

Components of the Model

We divide the model into three components: (1) baseline or the current stock of supply and
demand, (2) estimates of excess demand, and (3) projected supply and demand. We address each of
these components below.

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Baseline Supply and Demand
Our baseline estimates will be developed for 2010, the most recent year for which claims data
are available for our analysis. We provide an overview of our approach to measuring baseline supply
and demand here.
Baseline Supply. Because no specific credential or specialty exists that is common across
physicians providing HIV care, we will identify physicians who appear to focus on HIV care based
on the services they provide as reported in ambulatory medical and pharmacy claims data. Because
nurse practitioners and physician assistants often do not bill directly for their services, we will
supplement our estimate of the number of nurse practitioners and physician assistants currently
providing HIV services based on data reported in this study’s clinician survey, using the average
number of nonphysician clinicians providing HIV care per physician providing HIV care.
Baseline Demand. We will develop two estimates of baseline demand: market-based and
needs-based demand. Market-based demand, as defined in this study, is the effective demand for
services observed in the healthcare market today. We will estimate market-based demand based on
observed utilization of HIV services nationally. The foundation for our needs-based demand
estimate will be the HRSA clinical guidelines for the treatment of HIV/AIDS. We will review these
guidelines and current utilization levels with clinical experts to obtain their input on how the
guidelines translate into clinician time and how observed utilization patterns today would need to
shift to achieve the optimal standards reflected in the guidelines.
Excess Demand
We will also develop two estimates of current excess demand: market-based and needs-based.
Excess demand is the amount of demand for care that cannot be met by current supply. The
market-based estimate will rely primarily on findings from the clinic survey as measured by (1) the
difficulty of hiring clinicians, (2) the difference between the number of open positions and the
number of new entrants, and (3) measures of patient access to care. The needs-based estimate of
excess demand will be calculated as the difference between the baseline estimate of needs-based
demand and the baseline supply of care.
Projected Supply and Demand
We will project supply and demand from 2010 through 2015. To capture the effect of
retirement among the second half of the baby boom generation, we will also discuss with HAB the
value of projecting HIV clinician supply through 2020. We provide a brief overview of our approach
to projecting supply and demand here.
Projected Supply. We will estimate active clinician supply in the next year as clinician supply in
the current year plus new entrants minus attrition. We will calculate our estimate of new entrants as
a share of recent graduates. We will base our estimate of attrition on retirements and mortality.
During the expert consultation meeting, the majority view was that mid-career entrance into and exit
from HIV clinical care was rare; participants explained that most clinicians enter HIV care early in
their career and remain in HIV care until they retire. Thus, we do not plan for these mid-career
shifts to be a significant component of entry and exit in the model. Nevertheless, we will test this
conclusion in the clinician survey. Supply projections will also allow for simulation of the impact of
changes in productivity and substitution of supply across provider type and medical specialty on the
capacity of the HIV clinician workforce.

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Projected Demand. We will develop two distinct estimates of projected demand: marketbased and needs-based. We will base both estimates on the same set of factors. However, the
baseline assumptions for the two projections will be distinct. We will derive market-based demand
estimates from observed utilization patterns in the market. We will derive needs-based demand
estimates from normative assumptions and recommended treatment guidelines about the optimal
use of services among people with HIV and AIDS. We will adjust both estimates for changes in
population size, prevalence of HIV, and service use per individual with HIV over time relative to
their baseline estimate. For both demand projections, the model will allow for simulating the impact
of changes in diagnosis rates, economic growth rates, the distribution of insurance coverage, and
advances in clinical treatments for HIV.
In the next section, we further detail our approach to each of these components.

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II. MODEL DESIGN
This section discusses our approach to model development. We first discuss our plan for
estimating baseline supply and demand. Then, we discuss how we will estimate whether current
supply and demand are in equilibrium. Finally, we discuss our methodology for projecting HIV
clinician supply and demand from 2010 to 2015.
A. Baseline Estimates of Supply and Demand
In this section, we present our approach to estimating the baseline supply of and demand for
HIV clinicians. To estimate this baseline, we use observed data on the number of clinicians currently
providing HIV-related health care services and the number of services they currently provide.
Because there is a lag between the provision of health care services and the availability of data for
research on these services, the most recent period of observed data available for analysis from most
of the sources for this study will be 2010. 1 Therefore, baseline estimates will be for 2010. We will
project supply and demand from 2010 through 2015.
1.

Baseline Supply of HIV Clinicians

Within the baseline HIV clinician supply model, we will develop baseline counts (or stock) of
currently practicing clinicians with the following dimensions:
• Age/Gender. Within the model, counts of physicians will be available by year of age
and gender. Counts of nonphysician clinicians may not available by age/gender.
• Provider Specialty/Type. We will organize the clinician workforce into four
types/specialties. These will be (1) physicians specializing in infectious disease; (2)
primary care physician (including internal medicine, family/general medicine, pediatrics,
and geriatrics); (3) nurse practitioners, and (4) physician assistants.
• Geographic Location. We will develop clinician counts for the eligible metropolitan
areas (EMAs) and transitional grant areas (TGAs) defined under the Ryan White
program, as well as other metropolitan statistical areas (MSAs) defined by the United
States census bureau. For each state, we will group rural areas not otherwise included in
these metropolitan jurisdictions and analyze them separately.
• Type of Practice. Because individual clinicians may organize their time across care
settings differently, we will develop estimates of the total number of clinician hours and
the share of total hours dedicated to HIV patient care by type of primary practice. We
will define the practice type categories based on the survey data, but they may include (1)
community health centers, (2) hospital-based ambulatory care clinics, (3) communitybased organizations, (4) health department clinics, and (5) private physician practices. We
will also consider developing separate supply estimates for Ryan White-funded clinics
versus non-Ryan White-funded primary and specialty ambulatory care settings.

This assumes we will use a proprietary national all-payer claims database developed by SDI for this study.
Because of the greater lag in availability of Medicare and Medicaid claims data directly from the Centers for Medicare &
Medicaid Services, the most recent public claims data for this study would be 2009.
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Including this level of detail within the model will enable us to profile the clinician workforce by
age, gender, provider type and specialty, geographic location, and type of practice. We can also use
details on clinician characteristics, such as age and gender, to develop a foundation for projection
assumptions, such as retirement rates.
We will measure the baseline count of HIV clinicians in two ways. First, we will present the
“active supply.” This count is simply the number of active clinicians in each year providing a
minimum threshold of HIV care. The second measure is the full-time equivalent (FTE) supply. This
measure normalizes the count of clinicians by average weekly hours worked in HIV care per
clinician relative to the average hours worked by all HIV-related clinicians in all types of patient care
in 2010. For example, if a given HIV clinician works 30 hours per week in HIV-related care and the
average number of patient care hours worked across all HIV clinicians is 40 hours per week, the
FTE supply of that given HIV clinician would be 0.75 or 75 percent of the active supply. The FTE
supply provides a more precise measure of the supply of HIV services that each active clinician can
be expected to produce. If many HIV clinicians work part-time or devote a substantial share of their
time to general primary care or other infectious disease care, for example, then the level of HIV
services each active clinician can be expected to provide will be reduced accordingly. Next, we
describe our approach to estimating the baseline active supply. Then, we discuss our approach to
estimating the baseline FTE supply.
Estimating Baseline Active Supply
One of the most difficult challenges in the model development is identifying how many
physicians, nurse practitioners, and physician assistants provide HIV-specific services nationally. The
master file of the AMA is often used to provide an estimate of the current number of physicians of a
given medical specialty. Information on board certification, fellowship/residency training, or selfreported specialty in the AMA file is used to infer medical specialty. However, there is no explicit
credential or self-reported specialty for those who provide, focus on, or specialize in the provision
of services to HIV patients. Moreover, many of those who focus on providing HIV-related health
care services do not do so exclusively. A primary care physician might, in addition to providing care
to a significant number of HIV patients, provide primary care services to a general patient
population, and an infectious disease specialist who focuses on HIV might also treat patients with
other infectious diseases. Although the HIVMA and AAHIVM offer credentialing in HIV medicine,
many physicians providing HIV care do not have this certification. This is also true for physician
assistants and nurse practitioners providing care to HIV patients. There is no specific required
credential or list of professionals that we can use to estimate the baseline supply of HIV clinicians
for this study.
As a result, we propose using a two-tiered approach to identifying the baseline supply or stock
of HIV clinicians. The first tier will focus on physicians and mid-level clinicians who independently
bill for their services. We will organize physicians into two groups: primary care and infectious
disease specialists. The second tier of clinicians will include physician assistants and nurse
practitioners, some of whom may not be able to bill independently or do not bill under their own
name. We will base our approach to identifying the number of clinicians in the first tier primarily on
prescription drug and other HIV-related ambulatory medical claims data. These data enable us to
link the delivery of a particular type of service—medical treatment of HIV patients—with the billing
clinician to discern which clinicians focus on the provision of services to patients with HIV.
Physicians and mid-level providers who bill independently are identifiable in claims data. Because
clinicians in the second tier often do not bill for their services independently, we will estimate the
number of each of these clinician types currently providing HIV services based on data reported in
the HIV clinic workforce survey to be conducted as part of this project.
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Tier One: Baseline Count or Stock of HIV Physicians. 2 Given the lack of an established
credential for physicians and other nonphysician clinicians providing HIV services, we propose to
define HIV clinicians based on the services they provide and for which they bill. Using prescription
drug and other ambulatory medical claims data, we will identify HIV services based on HIV-related
diagnosis, procedure, and drug codes. Then, for each clinician providing and billing for HIV care,
we will determine the total number of visits and/or prescriptions and the percentage of the visits
and/or prescriptions provided that are for HIV care. We will include clinicians exceeding a
minimum threshold in the number of visits and/or prescriptions or the share of visits and/or
prescriptions that are for HIV care in our list of HIV clinicians. Alternatively, we can use the
number and share of patients treated for HIV to identify HIV clinicians. We will determine this
minimum threshold empirically (in consultation with HAB and clinical experts) based on an analysis
of the claims data; it could vary by provider type. We will establish this threshold high enough to
filter-out episodic providers (such as emergency department physicians or medical residents), but
low enough to capture a substantial majority of HIV care. We will also test various combinations of
thresholds based on pharmacy and medical claims and assess the effect of each algorithm on the
selected list of clinicians before making the final determination. 3
The detail on clinician characteristics will enable us to profile the HIV clinician workforce by
age, gender, provider type and specialty, geographic location, and type of practice. We will also be
able to also use the details on clinician characteristics to develop a foundation for projection
assumptions, such as differences in retirement rates by age category or differences in number of
hours worked between men and women. We are currently reviewing these sources for this
information.
The services included in the claims data that we will use to identify HIV clinicians do not
comprehensively reflect HIV services provided nationally. As a result, by using claims, we might
inadvertently exclude subsets of clinicians who provide services to individuals not represented in
these data, such as the uninsured. To address this limitation, representatives of HIVMA and
AAHIVM have agreed to provide data from their membership lists for this study. We will compare
their membership lists with the list of clinicians providing HIV services derived from the claims
analysis. If substantial numbers of physicians in the membership lists are not included on the list
derived from the claims analysis, we will work with the medical associations to understand these
gaps. We will supplement the clinicians identified through the claims analysis with those who appear
on the organization membership lists only. We will also match and supplement the claims-based list
of clinicians with individuals who attended the 2010 national HIV/AIDS clinical conference and/or
participated in the regional AETC training sessions.
This list of physicians and nonphysician clinicians who independently billing for HIV services
will give us our baseline count or stock of active supply of Tier One HIV clinicians.
Tier Two: Baseline Count of Nonphysician HIV Clinicians. The claims analysis will
identify some nonphysician clinicians who can bill independently. We will include these clinicians in
our list of tier one HIV clinicians. However, we expect representation of these nonphysician
2 The baseline supply identified in tier one will include a limited number of physician assistants and nurse
practitioners who can bill independently. These clinicians will be deduplicated from those identified in tier two.

Appendix A includes a detailed description of the analytic approach for identifying HIV clinicians in claims data.
Appendices B and C contain, respectively, a comprehensive list of diagnosis/procedures codes and drug codes for HIV
disease.
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clinicians to be limited and potentially biased. We will address this limitation through the HIV clinic
survey. This survey will be sent to a sample of the clinics in which the clinicians identified in tier one
practice. To estimate the number of nonphysician clinicians providing HIV services who are not
billing independently, we will include the following question on the survey (see Table II.1):
• We are interested in the number of clinician FTEs in this clinic and the share of these
FTEs that is allocated to caring for patients with HIV or AIDS. In column A, please
indicate the number of clinician FTEs in this clinic providing patient care in general. In
column B, please indicate the number of clinician FTEs devoted to HIV patient care.
Table II.1. Number of FTE Clinicians in Total and HIV Care

Type of Clinician

Infectious disease specialists

Column A
Number of FTE clinicians
in total patient care

Column B
Number of FTE clinicians
in HIV patient care

Primary care physicians
Physician assistants
Nurse practitioners

Note: Primary care physicians include internal medicine, family/general medicine, pediatrics, and geriatrics.

We will use responses to this survey question to estimate the ratio of nonphysician HIV
clinicians to physicians providing HIV services. We will assess the variation in this ratio across
practice settings and geographic areas (for example, regions and urban versus rural areas) and
incorporate it into the baseline estimate of nonphysician clinician supply. Then, the number of
nonphysician clinicians nationally will be calculated for each geographic areas and practice settings
and nationally, based on the number of physicians identified in tier one and this ratio.
Estimating Baseline FTE Supply
The clinic survey questions related to nonphysician clinicians will ask for FTE clinicians only.
That is, if an administrator respondent has two part-time nonphysician clinicians in his or her
practice each working 20 hours per week, the respondent will be asked to report this as one FTE
clinician assuming full-time is 40 hours per week. 4 Thus, estimates of nonphysician clinicians (tier
two) will be produced only as FTE supply. To translate the active supply of HIV physicians (tier
one) into FTE supply, we will need to apply two supply-intensity adjustments for each HIV
physician identified in our baseline count of active HIV physicians. These adjustments will be
• Percentage of total time spent in HIV-related patient care 5
The number of hours worked by a full-time clinician is likely to be in excess of 40 hours per week. We will base
our estimate of the average hours worked by a full-time clinician on the observed number of hours worked reported in
the clinician survey.
4

Our previous work with specialists and subspecialists indicates that some provide “nonspecialty” care (that is,
primary care) to round out the time they have available. As greater opportunity for providing care in their specialty
arises, they reduce the amount of nonspecialty care and provide more specialty care. We do not know if this type of
relationship holds for infectious disease specialists who focus on HIV or for erstwhile primary care physicians who focus
on HIV. We will address this issue in the survey, but it is also useful to interview some physician clinicians in HIV to
understand their perspective on this issue, as it will affect the implied supply of HIV clinicians.
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• Expected hours worked per week relative to the average HIV physician
Percentage of Time in HIV Patient Care. We will estimate the percentage of time a clinician
spends in HIV patient care based on the claims analysis. For each physician specialty in the study,
we will establish a threshold share of visits, prescriptions, or patients with HIV diagnoses or services
above which the clinician will be considered to be fully engaged in HIV care. Then, for each active
clinician identified in the claims analysis, we will calculate the share of visits, prescriptions, or
patients for HIV patient care relative to this threshold to yield the clinician’s percentage of time in
HIV care. For example, if the threshold number of visits per year is 100, then we will consider a
clinician with 50 visits reported on the claims data to be 0.5 FTE. Alternatively, we can determine a
patient threshold (say, 20 patients in care) to identify full-time HIV physicians. If we set the patient
threshold at 20 patients, then we would consider a physician with only 10 unique patients observable
from the claims data to be 0.5 FTE. We will use findings from the national HIV clinician workforce
survey to refine this calculation. For physicians identified in the claims analysis we will estimate this
measure empirically. For physicians identified through other potential sources—such as HIV
provider association membership—we will assign the average share of time in HIV care for
physicians with the same personal and practice characteristics.
Expected Hours Worked Relative to the Average HIV Physician. The expected relative
hours worked for each active physician will be assigned based on the estimated average hours
worked for physicians of the same age and gender. We will develop estimates of hours worked
specific to HIV clinicians based on our study’s HIV clinician survey by including questions on age,
gender, and mean weekly hours spent in all patient care generally and HIV patient care specifically
across all practice locations. The benefit of using our study’s survey is that the results will be specific
to physicians providing HIV care. We will compare the results from our survey to national norms
for physician hours worked for primary care physicians and medical specialties from existing
surveys. Existing surveys include a survey sponsored by the Bureau of Health Professions (BHPr)
during 2002 and 2003 that collected information on patient care hours worked and the
AMA/AAMC’s Survey of Physicians Over/Under 50.
We will use Equation (1) to calculate FTE clinician supply based on these two adjustments. The
FTE supply for each physician equals the share of time in HIV-related patient care multiplied by the
ratio of expected hours spent in HIV-related care relative to the average hours worked for all HIV
physicians. We will add FTE supply across physician HIV providers (i) to yield the estimate of total
FTE supply of HIV physicians.
Eq. (1)

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Figure II.1 below illustrates our approach to estimating the baseline supply or stock of FTE
physicians.
Figure II.1. Approach to Estimating FTE Physician Supply

2.

Baseline Demand for HIV Clinicians

Within the model, we will develop baseline estimates of demand based on counts of patients
with HIV or AIDS. We will disaggregate patients into cells based on the following dimensions:
• Patient Age and Gender. Within the model, we will develop estimates of demand by
patient age group and gender.
• Geographic Location. We will develop HIV patient counts by geographic location at
multiple levels. Geographic areas modeled will include EMA and TGA jurisdictions
funded under the Ryan White program, MSAs, and nonmetropolitan areas within each
state. Using Part A and MSA designations, which can cross state boundaries, will help us
develop estimates based on actual patient flow patterns. We will also develop demand
estimates for all non-metropolitan areas within each state.
• Insurance Coverage. We will estimate the distribution of HIV patients by insurance
status and type of insurance, including privately insured, Medicaid only, Medicare, and
uninsured.
• AIDS Status. We will organize HIV patients into two groups, one based on diagnosis of
HIV infection only and one based on having an AIDS-defining condition.
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For each of these cells or subgroups, we will estimate the level of HIV care demanded and how
this care is currently distributed by provider type.
We will estimate baseline market demand for clinical services per individual with HIV infection
or AIDS diagnosis based on two separate components. These components are
1. Population. This measure includes the number of individuals living with HIV infection
only or with an AIDS-defining diagnosis.
2. Utilization of HIV Services. This measure can be based on counts of visits or
prescriptions or on medical expenditures. All utilization measures will be for HIV care
only and will vary by patient subgroup.
We will disaggregate both of these components (prevalence and service use) into cells defined
by patients’ age group, gender, geographic location, health insurance coverage, and AIDS status. We
will divide the estimate of service utilization for each of these cells by the estimate of the United
States population living with HIV infection only or an AIDS-defining diagnosis for the respective
cell to calculate the demand per individual living with HIV infection or AIDS diagnosis in each cell.
Baseline estimates of demand will reflect only those individuals who are currently diagnosed and in
care. In the projections of future demand, we will consider how increased screening and diagnosis
and improvements in retention in care will affect demand. Next, we describe in more detail the data
sources for developing population and utilization of service estimates.
Population
We will obtain counts of the number of individuals living with HIV infection and AIDS from
the Centers for Disease Control and Prevention (CDC). These counts are available by age group,
gender, geographic location, and AIDS status. The CDC does not provide information on the
insurance status of individuals with HIV. We will review the literature to determine whether another
source for estimating this dimension exists.
Service Utilization
We will consider three potential sources for estimating market utilization of services among
individuals living with HIV infection or an AIDS diagnosis:
1. National Center for Health Statistics (NCHS) National Health Care Utilization
Surveys. NCHS offers three nationally representative provider surveys of health care
utilization, each representing a different type of care. These are the National
Ambulatory Medicare Care Survey (NAMCS), National Hospital Ambulatory Medicare
Care Survey (NHAMCS), and the National Hospital Discharge Survey (NHDS). We will
use information on patient diagnoses in these surveys to identify HIV-related care.
These surveys also provide information on patient demographics, such as age, gender,
urban/rural location, and health insurance coverage. Physician specialty is included so
that utilization can be disaggregated by type of clinician. The strength of these surveys is
that they are nationally representative and they include uninsured patients who would
receive services through Ryan White clinics.
2. Medicaid, Medicare, and Private Health Insurance Claims Data. We can also use
the claims to identify service utilization per unique HIV or AIDS patient represented in
these data. The claims data include patient demographic information, such as age,
gender, and location. The claims also include physician specialty, so we can disaggregate
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utilization by physician specialty. However, the population included in claims data may
not be nationally representative; these data sources would not include information on
services provided to uninsured patients and medical services not covered by health
plans.
3. Ryan White HIV/AIDS Program Services Reports. Ryan White HIV/AIDS
Program Services Reports (RSRs) include client-level data on client characteristics and
services provided to clients of Ryan White-funded clinics. These data reflect only
services provided through the clinics. Despite that limitation, this is an important
segment of the current care system and may provide a useful source of information for
estimating demand for medical care.
The NCHS surveys are likely to provide a good source of data for the market demand analysis
because they are nationally representative and include a variety of insurers and care settings.
However, the data in the NCHS surveys provide less detail on providers and services than is likely to
be available through the other sources. We believe these data comprehensively represent HIV
services. Pooling multiple years of the NCHS survey data might be necessary to get a sufficient
sample of HIV services.
The NCHS surveys provide estimates of market utilization nationally. The Medicaid, Medicare,
and private health insurance claims data also provide a promising source for the demand analysis.
However, we would have to adjust utilization estimates derived from these data based on the subset
of the national population represented. For example, the claims analysis will not represent services
provided to the uninsured (including those covered under the Ryan White program), but the NCHS
survey data will include these services. Similarly the claims analysis includes services for only a subset
of the commercially insured population. Given the limitations of each of the three data sources, no
single data source is likely to be able to provide a reliable estimate of the national average level of
service utilization per individual living with HIV. Thus, the sources will be used in combination to
develop nationally representative estimates of market-based service utilization per individual living
with AIDS.
B. Estimating Excess Demand
Mounting evidence suggests that HIV clinician supply might not be keeping pace with the
growth in demand for HIV-related health care services. In the general literature, studies in the 1990s
predicted shortages of primary care physicians and surpluses of specialists by the end of the 1990s
(see Greenberg and Cultice [1997] for an example of this research). However, by the early 2000s,
new approaches to studying supply and demand of health care clinicians predicted shortages of all
types of physicians (Cooper et al. 2002). The more recent literature emphasizes that physician work
effort might be declining because the workforce is aging, more likely to be employees (rather than
self-employed or in partnerships), experiencing greater pressure on personal time, facing greater
complexity in treatment, and retiring. These and other pressures on clinician supply are believed to
be particularly true for HIV-related care (HRSA CARE Action April 2010).
In a letter to Congress requesting greater support, HIVMA concluded
Both the increase in patient load and the demands of HIV medicine are exacerbated by retirement and
burnout among the first generation of HIV clinicians. Many of us from the first generation of HIV care
clinicians will be retiring during the next decade, and there is not a sufficient and qualified pool of HIV
medical clinicians to take our places (HIVMA 2008).

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Mathematica’s study for HAB on the effect of state health reforms on access to care found that
HIV care clinicians across the country face increasing administrative pressures associated with
credentialing, prior approvals, referrals, and billing (Gilman et al. 2008). Clinicians also reported
spending more time helping patients manage their treatment protocols when limitations on the use
of pharmaceuticals and other medical services are imposed, and helping clients navigate the
increasingly stringent requirements for Supplemental Security Income (SSI) and Medicaid eligibility.
In addition, clinicians reported that Medicaid reimbursement rates are insufficient to cover the cost
of treating people with HIV. The study concluded that low Medicaid payment rates, combined with
increased administrative responsibilities, contribute to a lack of qualified clinicians—particularly
medical specialists and clinicians in rural areas—willing to treat people with HIV. Mathematica’s
qualitative assessment of clinician workforce capacity issues in Ryan White program care settings for
HAB echoed many of these findings (Gilman et al. 2009).
Excess Demand or Supply
We suggest using two approaches to estimating baseline excess demand or supply. The first
approach will use observed data, clinical guidelines, and expert opinion from clinicians to estimate
the level of care that would be minimally adequate to meet the needs of those currently living with
HIV or AIDS and compare this level of services with the level currently provided. The second
approach would look at market-based indicators of excess demand or supply. Many health care
workforce studies focus on market-based measures of supply and demand. However, because this
study is motivated by a public health concern about the adequacy of treatment for HIV patients, we
believe it is appropriate to estimate a needs-based model as well as to develop a market-based
estimate. We describe these two approaches in turn.
1.

Needs-Based Estimate of Demand

A needs-based approach would use a combination of observed data, clinical guidelines, and
expert opinion to estimate demand per individual with HIV infection or AIDS, as well as to estimate
the need for AIDS treatment for currently undiagnosed individuals. They will differ from market
demand because of two components. First, undiagnosed patients or patients who have been
diagnosed but are not currently in treatment will be the major component of needs-based demand
that is not included in observed market demand. The second component will be the implications on
needs based demand of treating HIV patients according to accepted guidelines or protocols of
appropriate standards of care. Clinical guidelines published by HRSA will be the foundation of the
needs-based estimates developed under this study. However, the guidelines only provide guidance
on the frequency of appointments and specify the treatment goals for certain clinical phases. The
guidelines do not specify a particular level of clinician effort or a volume of clinical services
recommended. As a result, they cannot be used directly to an estimate of needs-based demand, but
rather need to be translated into an estimate of clinician time.
As a starting point for the needs-based estimates, we will examine observed data on the current
volume of treatment being provided in Ryan White-funded clinics separately for patients diagnosed
with HIV only and those diagnosed with AIDS. With input from clinical experts, we will compare
this observed level of treatment to the HRSA treatment guidelines and the HAB performance and
HIV Quality Improvement (HIVQUAL) measures. We will ask the clinical experts to assess how the
level of care currently being provided deviates from the HRSA treatment guidelines and how the
level of clinician time per person diagnosed with HIV only or those diagnosed with AIDS would
need to change to meet the guidelines. Based on this input, we will develop a range of estimates for
needs-based demand under alternative assumptions as recommended by the clinical experts.
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The second and larger source of needs-based demand is to include those patients who are
currently undiagnosed or diagnosed but not in treatment. We will use CDC estimates of the
undiagnosed population, as well as the targets set forth in the national HIV/AIDS strategy, to
measure unmet need among the undiagnosed. We rely on use estimates from the literature, as well as
the targets from the national HIV/AIDS strategy, to capture the demand that would occur if those
who are diagnosed but not in regular care were to receive the appropriate levels and duration of
treatment.
2.

Market-Based Estimate of Demand

We will use data from this study’s clinic survey to develop our market-based estimate of excess
demand for HIV providers. Our clinic survey will include questions to collect the following
information, which will help us to develop this estimate: 6
• Assuming no change in current resource levels, such as funding or HIV medical clinician
FTE, what is the total number of new HIV-positive patients that your clinic would be
able to absorb?
• Is your clinic currently accepting new commercially insured patients with HIV? Medicaid
patients? Medicare patients? Uninsured patients?
• What is the average waiting time (in weeks) for scheduling appointments for each of the
following types of patient: newly diagnosed patients, patients new to your clinic but not
newly diagnosed, and established patients?
• What is the average length of the typical visit for each of the following types of patient:
newly diagnosed patients, patients new to your clinic but not newly diagnosed, and
established patients?
• How difficult is it to recruit HIV primary care clinicians (physicians, nurse practitioners,
and physician assistants) or infectious disease specialists? How easy is it to retain HIV
primary care clinicians or infectious disease specialists?
• Please indicate in column A the number of HIV-care related clinical vacancies (by FTE)
in your clinic that are the result of retirement or staff expansion (as opposed to
turnover), and in column B the average length of time these positions have been vacant.
Please limit vacancies to only to positions for which funding exists.

6

The HIVMA Workforce Survey conducted in 2009 included the first four of these questions.

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Table II.2. Open Vacancies for Funded HIV Clinicians and Length of Time to Fill Position

Type of Clinician

Column A
Current FTE Vacancies
Resulting from Staff
Retirement or Expansion

Column B
Average Length of Time
Position(s) Has Been Vacant
(in months)

Infectious disease specialists
Primary care physicians
Physician assistants
Nurse practitioners

We will regard as excess demand the difference between the number of new positions or
positions available as a result of retirement and the number of individuals expected to complete
training and enter HIV care in the current year. For example if we find that clinics and physician
offices have 75 open positions that are new or related to retirement (not related to staff turnover)
for primary care physicians providing HIV care, but we estimate that only 50 of the primary care
physicians completing their training in the given year will enter HIV care, we would estimate a
shortage of 25 primary care physicians in the baseline year. The results from this analysis will be
validated against information reported in other survey questions such as questions related to patient
access to care, appointment waiting times, and difficulty hiring new clinicians.
C. Projecting Supply and Demand
In this section, we discuss our proposed approach to projecting supply and demand from the
baseline year 2010 to 2015.
1.

Projecting Supply of HIV Clinicians

We will project the supply of HIV clinicians from 2010 through 2015, and will discuss with
HRSA the benefit of projecting supply through 2020 to capture the retirement rates associated with
the second half of the baby boom generation. Similar to baseline supply, we will include measures of
both active and FTE supply. We first discuss how we will project active supply. Then, we discuss
how we will adjust the active supply estimate to produce an estimate of FTE supply.
Mathematically, active supply in the next year (t + 1) is a function of supply in the current year
(t) plus new entrants minus attrition:
Eq. (2)
New entrants are physicians completing fellowship training who chose to enter HIV care, as
well as currently practicing physicians who shift into HIV medicine. Attrition is physicians who have
retired, changed careers, shifted out of HIV medicine into another medical specialty, or died. Since
mid-career shifts in clinical specialty are atypical, our projections will focus on new entrants who
have just completed clinical training, mortality, and retirement as these are likely to cause the most
substantial shifts in supply.

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New Entrants into HIV Care
Similar to our method of estimating baseline supply, we will use a two-tiered approach to
estimate new entrants, with different approaches for estimating new entrants for physicians and
nonphysician clinicians.
Tier One: Estimating New Physician Entrants. We will base our estimate of the number of
new physician entrants to HIV care in each year between 2010 and 2015 on the following
components:
• Share of Physicians Completing Training and Entering HIV Care Between 2000
and 2010. For primary care physicians and infectious disease specialists in the claims
analysis we will empirically identify the cohorts of new entrants into HIV care for the
past 10 years based on the age of the physicians. For example if the youngest age
observed with a substantial number of physicians is 32, we would assume all 32-year-old
physicians entered in the most recent year, all 33-year-old physicians entered in the prior
year, and so on. We will compare the counts of physicians in these cohorts with the
number of clinicians completing training in the respective specialty in the particular year
to estimate the share of primary care physicians completing residency and the share of
infectious disease specialists completing fellowship training who entered HIV care over
the past 10 years. For example, if 100 infectious disease specialists reported that they
entered HIV care in 2005 and we know that there were a total of 300 infectious disease
specialists who graduated in 2005, then the share of infectious disease specialists entering
HIV care is 33 percent. We will assess whether a trend exists in this share and whether
we expect the factors that might influence this trend to continue between 2010 and 2015.
Based on this analysis, we will project the share of primary care and infectious disease
physicians completing training over the next 5 years who will begin providing HIV care.
• Number of Physicians Completing Training by Specialty Between 2000 and 2010.
We will also assess recent trends in the number of primary care physicians completing
residency and infectious disease specialists completing fellowship training annually.
Based on this analysis we will develop projections for the number of physicians
completing training in these specialties in each year between 2010 and 2015.
We will multiply our projections of the number of specialists completing training each year by
the share of each of these specialties projected to enter HIV care to project the number of
physicians completing training who will become new entrants in HIV care in each year between
2010 and 2015. In addition to estimating the number of physicians entering HIV medicine, we will
develop estimates of the mean number of hours these clinicians will work and the share of their
hours that will be devoted to HIV care. The primary source for these estimates will be the clinician
survey. Based on this survey, we will estimate the age and gender distribution of new entrants, the
number of hours worked, and the share of these hours devoted to HIV care.
Tier Two: Estimating New Nonphysician Clinician Entrants. We will project the number
of new nonphysician clinician entrants to HIV care based on data collected in the clinic survey, as
well as on national policy changes, such as the change in the community health center physician to
PA/NP staffing ratio from a current ratio of 1:1 or 1:2 to a ratio of 1:4 as documented in the Access
Transformed report by the National Association of Community Health Centers (NACHC).
We will ask clinic survey respondents to answer the following questions about the nonphysician
workforce in their clinic:

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• How many clinicians of the following types providing HIV care have been added (not
replacing another staff member) to the staff in your unit within the past 12 months:
o Physician assistants
o Nurse practitioners
Based on these survey responses, known policy changes, and the number of new mid-level
clinicians entering the workforce annually, we will develop a range for the number of new physician
assistant and nurse practitioner entrants annually between 2010 and 2015.
Attrition: Retirement and Mortality
We model attrition from HIV care provision related to two primary sources: retirement and
mortality. For each of these types of attrition we address attrition for tier one and two types of
clinicians.
Tier One: Estimating Physician Retirement. We will develop baseline estimates of
retirement rates for physicians providing HIV care from two sources. The first source identifies
recently observed retirement rates among physicians generally. The second source will be specific to
physicians providing HIV services. It will reflect anticipated age of retirement in most cases rather
than observed behavior, because we will be surveying active clinicians. Because the latter source is
based on anticipated age of retirement, it may be less accurate than a source based on observed
retirement rates. Prior analysis conducted by The Lewin Group (2009) compared the distribution of
observed and anticipated ages of retirement. The analysis found that physicians intend to retire
earlier than predicted by historical observed retirement rates. Thus, we expect that the estimates of
anticipated age of retirement will project higher retirement rates than will likely occur. In contrast,
applying average retirement rates for all physicians to HIV clinicians may result in lower retirement
rate projections than will likely be observed because of the aging of the HIV clinician workforce and
the relatively high burn-out rate among HIV clinicians (Gilman et al. 2009).
We plan to use the following two sources:
1. AMA/AAMC Surveys of Physicians 50 and Over. Respondents to a survey of retired
physicians older than 50 conducted by the AMA and AAMC were asked to report the
age at which they retired. All other respondents were asked to report the age at which
they expected to retire. For those physicians ages 70 or older, we will use the reported
retirement age or the reported anticipated age of retirement if the physician is still active
to estimate the observed distribution of physicians by age of retirement. We will assume
that all physicians not yet retired will retire at age 75.
2. HIV Clinician Survey. In our survey of HIV clinicians, clinicians will be asked how
likely they are to reduce the number of HIV patients they serve in the next five years. If
they respond that they are somewhat or very likely to reduce their HIV patient load,
they will be asked if this is due to retirement. We will also ask them how likely they are
to retire from the health profession entirely within the next five years. We will use these
responses to estimate the number of HIV physicians expected to retire in the next five
years.
Tier Two: Estimating Nonphysician Clinician Retirement and Career Change. As part
of the clinic survey, we will ask respondents to provide the age distribution of the physician
assistants and nurse practitioners who work in their clinic. We will use this information to project
rates of retirement for these mid-level clinicians. We will develop estimates for nonphysician
provider attrition associated with retirement from the following sources:
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• Bureau of Labor Statistics Data on Retirement Rates. We will apply overall rates of
retirement across all professions by age to the nonphysician HIV clinicians to determine
attrition related to retirement.
• Parameter Estimates from the Nursing Supply Literature. A rich literature on
nursing supply associates factors such as economic conditions with nursing supply. We
will review this literature and adapt parameter estimates developed in this literature for
use in our model.
Attrition: Mortality
We will estimate separate mortality rates based on CDC estimates by age and for men and
women. We will apply average mortality rates to estimates for tier-two providers based on their age
at baseline. To adjust for lower occupational risk of mortality for physicians, their greater access to
quality health care services, and their generally better health associated with affluence, we will adjust
the average mortality rates for the physician providers in tier-one to 80 percent of the national
average for each age group. This adjustment is based on work by Johnson et al. (1999), which found
that mortality rates among people ages 25 to 64 are lower for physicians and other professional and
technical occupations compared with mortality rates in most nonprofessional occupations. For white
males, age-adjusted mortality rates for professional and technical occupations are approximately 75
percent as high as the rates across all occupations. For white females, the mortality rates for
professional and technical occupations are about 85 percent as high as rates across all occupations.
Mortality rates for women are lower than those for men.
Attrition: Overall
We will apply losses related to mortality to the baseline supply of physicians by age and gender
in 2010. Then, we will apply retirement and change-of-profession rates to the remaining supply of
physicians to calculate the number of physicians remaining in the workforce in 2011. These
adjustments will again be applied to the 2011 projection to obtain the remaining workforce in 2012.
We will repeat this process until we can calculate the workforce remaining in 2015.
FTE Supply
After projecting an estimate of active supply for each year between 2010 and 2015, we can
translate this measure into FTE supply. Under the previous discussion of baseline supply, we
discussed the potential data sources for estimating hours worked by the age and gender of the
provider. We will start by multiplying the number of physicians in each age and gender category in
each projection year by the estimated hours worked for their respective age and gender groups. We
will then sum the products for each age and gender group across all the groups. Finally, we will
divide this total by the average hours worked across all physicians in the baseline year to estimate the
FTE supply in each year of the projection. Thus, the FTE supply in each future year (t) is equal to
multiplying the active supply in year (t) by the adjustment for changes in average patient care hours
worked in the baseline year.
Eq. (3)

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For example, if clinicians increasingly work part-time in HIV care, we might find that the ratio
of patient care hours per clinician in 2011 is 90 percent of that for the base year 2010. Then, if the
active supply of clinicians in 2011 is 1,100, the effective FTE supply will be only 990 (90 percent of
1,100).
We will review the general literature on physician supply and incorporate any findings on
generation shifts in hours worked into our model. We will also test the sensitivity of our findings to
potential shifts in hours worked among clinicians.
Productivity Change and Substitution Across Provider Types
Based on information collected on this study’s clinic survey, we will estimate a production
function for HIV care. Gilman and Green (2008) estimated a similar model to identify the
determinants of cost variation among programs that offer early intervention services to people living
with HIV and AIDS in the United States. Their model found that practice setting and patient
characteristics had a significant impact on average costs, measured in terms of both costs per visit
and costs per client. Hogan and Bouchery (2009) estimated a production function for cardiology
services. This model provided estimates of the marginal productivity of cardiologists, nurse
practitioners, and physician assistants in the practice setting.
For this study, we will estimate a production function for HIV care at the clinic level. We will
test the variability of the model results based on alternative measures of level of HIV care produced.
These measures may include number of HIV care visits, total revenue, and/or total relative value
units (RVUs), as feasible given the data. The level of HIV care produced will be a function of
• Physician hours worked in HIV care
• Nonphysician clinician hours worked in HIV care
• Practice setting characteristics (for example, primary/specialty care, practice size, Ryan
White clinic, share of patients non-English speakers, and hospital- versus communitybased)
• Practice location characteristics (for example, urban/rural, and local HIV prevalence)
• Patient mix (proportion new to care, AIDS diagnosis, and other comorbidity)
• Meaningful use of health information technology (such as electronic medical records and
telemedicine)
• Implementation of streamlined scheduling procedures (such as open booking)
• Use of improved workflow strategies (such as task shifting and task sharing)
• Use of care coordination and management models (such as medical homes and patientcentered navigation)
Parameters estimated in the model will indicate whether each factor has a significant impact on
HIV workforce productivity. We will use the size of these parameter estimates to model the impact
of improvements in efficiency or shifts in a practice or patient characteristics between 2010 and
2015 on clinician supply.

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Summary
In this section, we summarize our approach to the supply side of the model. Table II.3 indicates
the factors that we will include in the model with the associated data sources and methods.
Table II.3. Summary of Supply- Side Factors
Factor

Baseline supply of
physicians

Measure

Data Sources

• Number of HIV primary care physicians • Medicaid Analytic eXtract (MAX) claims
data
• Number of HIV infectious disease
• Medicare Standard Analytic Files (SAF)

specialists

• SDI claims data

• Ingenix private insurance claims data

• HIVMA and AAHIVM membership lists

• List of attendees at 2010 HIV/AIDS
Clinical Conference and participants in
regional AETC trainings

Baseline supply of
nonphysician
clinicians

• Number of physician assistants per HIV • National HIV Clinician Workforce
Survey
physician

Base supply of
physicians
completing training

• Number of primary care physicians
completing training

• American Board of Internal Medicine
and Journal of the American Medical
Association

• Share of primary care physicians
completing training and providing HIV
services

• Share of infectious disease specialists
completing training and providing HIV
services

• Primary care and infectious disease
specialists younger than 42 providing
HIV services as a share of the overall
count of physicians younger than 42
in the respective specialty based on
claims analysis.

• Rate at which clinicians plan to retire

• AMA/AAMC Surveys of Physicians 50
and Over

Professional
entry/exit

• Rate at which nonphysician clinicians
will exit or enter profession

• National HIV Clinician Workforce
Survey

Hours worked

• Average number of hours worked per
week by age/gender

• Bureau of Health Professions Survey

Share of physicians
completing training
and providing HIV
services
Retirement rates

• Number of nurse practitioners per HIV
physician
• Number of infectious disease
specialists completing training

• Association data

• National HIV Clinician Survey
• Nursing supply literature

• AMA/AAMC Survey of Physicians
Over/Under 50
• National HIV Clinician Workforce
Survey

Note:

HIVMA = HIV Medical Association, AAHIVM = American Academy of HIV Medicine, AAMC =
American Association of Medical Colleges.

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Figure II.2 illustrates the steps we will take to project the supply of HIV clinicians through
2015.
Figure II.2. Approach to Projecting Total FTE Supply of HIV Clinicians

2.

Projecting Demand for HIV Clinicians
We will base our HIV-related health care demand projections on the following components:
• Demographic Trends. We will use United States census population projections to
estimate the size of the United States population by age and gender in each year between
2010 and 2015. To project future demand, we will use population projections stratified
by age, gender, and region.
• HIV Prevalence Rates. We will multiply CDC estimates of HIV/AIDS prevalence
rates in each age and gender group by the United States census population projections in
each age and gender group to produce estimates of the number of people living with
HIV infection or AIDS in each year from 2010 to 2015.
• Service Use per Individual with HIV. We will multiply estimates of service use per
individual by age group, gender, urban/rural location, health insurance coverage, type
and specialty of clinician (primary care physician, infectious disease specialist, and midlevel clinician), and HIV infection versus AIDS. We will derive the baseline demand
estimates based on currently observed levels of demand observed in Medicare, Medicaid,
and private insurance claims data, in NCHS survey data, and in RSR reports, by the
projected number of individuals in these cells to yield the total level of services that
those individuals will demand. We will translate the total level of services demanded into
an estimate of the number of FTE physicians demanded using the estimate of FTE
services provided by each physician also developed as part of the baseline demand
estimate.
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We will use these three components to produce the baseline demand projections. We will also
consider alternative scenarios incorporating the following additional demand-side factors:
• Trends in HIV Diagnosis. CDC estimates that approximately one-fifth of people
living with HIV in the United States are unaware of their serostatus; it recommends
implementing routine opt-out testing in nonprimary care settings. Prevalence estimates
might increase if more people are tested and diagnosed with HIV. We will develop
estimates of the likely impact of increased testing on prevalence from the literature. We
will also review estimates of the impact of testing and outreach initiatives on the cost of
serving the out-of-care population being developed by NIH, the CDC Medical
Monitoring Project, and the AHRQ HIV research network to develop a needs-based
estimate of the cost of treating individuals currently not in care. Finally, we will refer to
the targets put forth in the national HIV/AIDS strategy to develop scenarios for the
number of people who might enter care within the next five years.
• Economic Growth. Continued income growth in the United States will result in
increased demand for all types of medical care, including HIV care. In general, as income
increases, the demand for goods and services that people value also rises.
• Insurance Status. The Patient Protection and Affordable Care Act of 2010 (ACA) will
increase insurance coverage for many individuals living with HIV. This increase in
insurance coverage is likely to result in increased diagnosis and demand for services. In
the baseline demand calculations, we will estimate demand by type of insurance
coverage. We will use these estimates of the variation in demand by type of insurance
coverage to develop an estimate of the impact of the expected insurance coverage
changes under the ACA.
• Technological Advances. As new procedures are developed and prove efficacious,
demand for care could increase above the projected increases related to demographic
trends. Alternatively, new ARV treatments might require fewer doses and offer more
resistance, reducing demand for care.
Based on this list of epidemiological and clinical factors, we will develop various scenarios for
the demand for the HIV workforce. Next, we summarize our approach to the demand-side factors
of the model and the associated data sources and methods.
Table II.4 provides a summary of the demand-side factors that we will use to forecast the
demand for HIV clinical services, along with the data sources we will use to measure each factor.

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Table II.4. Summary of Demand- Side Factors
Factor

Demographic trends
HIV prevalence rates
Service use per HIV patient

Change in insurance status

Impact of increased testing
Change in treatment

Measure

• Population projections from
2010 to 2015 by age, gender,
region, and insurance status
• Recent HIV prevalence rates by
age and gender group
• Service use estimates
measured by visits by age
group, gender, urban/rural
location, health insurance
coverage, and physician
specialty for market-based
demand
• Service use estimates by age
group, gender, urban/rural
location, health insurance
coverage, and physician
specialty for future needsbased demand

• Increase in the number of
covered people from
Congressional Budget Office
estimates
• Increase in the number of
people diagnosed with HIV
that were previously
undiagnosed; difference in
age-specific prevalence
between insured and
uninsured populations
• Increased age- and genderspecific incidence leading to
higher prevalence
• Change in service use due to
maintenance regimen with
ARV therapy, adverse side
effects of ARV therapy, and
lower incidence of acute
illness/infection

Data Sources

• U.S. Census Bureau data
• Centers for Disease Control
and Prevention
• Claims analysis for marketbased demand
• RSR data for market-based
demand
• NCHS survey data for marketbased demand
• Input from clinical guidelines
and expert panel for needs–
based demand

• Difference in prevalence and
service use between claims
analysis (Medicare, Medicaid,
and private insurance claims)
and RSR data

• Model the impact of increased
testing
• Guidelines for ARV therapy
• Published literature and
clinical consultants

Notes: ARV = antiretroviral; RSR = Ryan White HIV/AIDS Program Services Report; NCHS = National Center
for Health Statistics.

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REFERENCES
Cooper, RA; Getzen, TE; McKee, HJ; and Prakash, L. “Economic and Demographic Trends Signal
an Impending Physician Shortage.” Health Affairs. 21(1) 2002:140-153.
Gilman B, Hargreaves M, Au M, Kim J, Mathematica Policy Research, Inc. Factors impacting the
retention of clinical providers and other key personnel in Ryan White HIV/AIDS Program care settings.
[unpublished]; March 6, 2009.
Gilman, Boyd H., and Jeremy C. Green. “Understanding the Variation in Costs Among HIV
Primary Care Providers.” AIDS Care: Psychological and Socio-Medical Aspects of AIDS/HIV, vol. 20,
no. 9, 2008, pp. 1050–1056. Retrieved from http://www.informaworld.com
/10.1080/09540120701854626 on January 10, 2011.
Greenberg, L. and J. M. Cultice. “Forecasting the need for physicians in the United States: the
Health Resources and Services Administration's physician requirements model.” Health Services
Research 31(6) 1997:723-37.
Greenberg, Leonard; Cultice, James M. "Forecasting the need for physicians in the United States: the
Health Resources and Services Administration's physician requirements model" Health Services
Research , Feb 1, 1997.
HRSA CareAction. “Workforce Capacity in HIV” US Department of Health and Human Services,
Health Resources and Services Administration, HIV/AIDS Bureau, Rockville, MD, April 2010.
Johnson, N.J., P. D. Sorlie, and E. Backlund. The Impact of Specific Occupation on Mortality in the
U.S. National Longitudinal Mortality Study.” Demography, vol. 36, no. 3, 1999, pp. 355–367.
The Lewin Group and the American Association of Medical Colleges. “Cardiovascular Workforce
Assessment: Final Report.” Falls Church, VA: March 2009.

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APPENDIX A
METHODOLOGY FOR IDENTIFYING HIV CLINICIANS FROM CLAIMS DATA

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Objective of the Claims Analysis
This analysis aims to identify providers (physicians and nonphysicians) who have a critical
volume of HIV-related visits, prescriptions and/or HIV-related patients. To meet this objective, we
will undertake a two-pronged approach using SDI claims data representative of those insured by
Medicare, Medicaid, and commercial insurance
Approach 1: Using the Physician Claims Database
Step 1: Identify HIV-Related Clinical Events at the Provider Level
1. We will create a national extract of ambulatory medical claims from the DX database.
2. Using International Classification of Diseases, 9th Edition, diagnosis codes and current
procedural terminology codes (see Appendix B), we will identify claims associated with
HIV-related clinical events. 7
3. We will perform a quality check on the extracted claims and remove duplicate claims.
4. We will merge information on medical specialty using the national provider identifier
code of the provider.
5. We will create a summary-level file (File 1) at the provider level with provider
information (for example, medical specialty) and total count of HIV-related visits (N).
We will arrange File 1 by health profession (physician, nurse practitioner, and physician
assistant) and medical specialty (internal medicine, general/family medicine, infectious
disease, pediatrics, and geriatrics).
Step 2: Identify Total Number of Services Provided at the Provider Level
1. We will create a finder file of providers and extract claims for all visits provided by the
providers.
2. We will create a variable that sums all the visits (D) provided by the providers and merge
it to File 1 using the national provider identifier.
3. The proportion of provider visits (P) that is dedicated to HIV-related events at the
provider level is N divided by D.
4. We will repeat these steps to calculate the number and proportion of patients who are
treated for HIV for each provider.

7 Guides from the Centers for Disease Control and Prevention (CDC) and the American Academy of HIV
Medicine (AAHIVM).

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Step 3: Define HIV Providers
Issues associated with defining HIV providers include the following:
• Should the threshold used to define an HIV provider be based on the absolute volume
of visits or patients (N) or the proportion of visits or patients related to HIV (P)?
• Should the threshold vary by health profession, medical specialty, and/or geography?
• Should the threshold be based on the distribution of N (for example, will providers with
N above the 25th percentile be defined as HIV providers)?
Approach 2: Using Pharmacy Claims Database
1. We will create an extract of pharmacy claims from the RX database as Approach 1.
2. We will identify pharmacy claims for HIV medications (see Appendix C) using the
national drug codes (NDCs). 8
3. Using the prescribing date as a marker, we will summarize the pharmacy claims for HIV
medications by prescribing date, patient ID, and prescribing clinician.
4. We will derive the total count of prescribing visits related to HIV by counting the
prescribing dates for HIV medications. This assumes that HIV medications were
prescribed during an HIV-related visit. The goal is to count the number of prescribing
visits for each provider.
5. We will link the file with provider information using the national provider identifier of
the prescribing clinician.
6. The resulting file (File 2) will include the number of visits related to HIV clinical events
at the provider level. In addition, it will include information on health profession and
medical specialty.
7. We will establish a minimum volume threshold requirement for inclusion in the study,
based on the number of visits, scripts, or patients and apply this rule to File 2 to define
our baseline HIV clinician population.

“Guidelines by the DHHS panel on Antiretroviral Guidelines for Adults and Adolescents – A working Group of
the Office of AIDS Research Advisory Council (OARAC).” Department of Health and Human Services, December 1,
2009.
8

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APPENDIX B
ICD- 9- CM AND CPT CODES FOR IDENTIFYING
TREATED PATIENTS WITH HIV INFECTION

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ICD- 9- CM Codes (Diagnosis)
042x to
HIV disease, with codes for the HIV-related manifestations or conditions, if the
044x
results are positive and the patient exhibits symptoms
V08
Asymptomatic HIV infection status if the results are positive but the patient is
asymptomatic
V01.79
Exposure to HIV virus
795.71
Nonspecific serologic evidence of HIV
V65.44
HIV counseling (if counseling is provided during the encounter for the test or
after the results are available)
CPT Codes (Laboratory tests)
86701
antibody HIV-1 test
86702
antibody HIV-2 test
86703
antibody HIV-1 and HIV-2 single assay
86689
Antibody; HTLV or HIV antibody, confirmatory test (for example, Western Blot)
87534
Infectious agent detection by nucleic acid (DNA or RNA); HIV-1, direct probe
technique
87535
Infectious agent detection by nucleic acid (DNA or RNA); HIV-1, amplified probe
technique
87536
Infectious agent detection by nucleic acid (DNA or RNA); HIV-1, quantification
87390
Infectious agent antigen detection by enzyme immunoassay technique, qualitative
or semi-quantitative, multiple step method; HIV-1
99211–
HIV counseling for patients with positive test results; office or other outpatient
99215
visit for the evaluation and management of an established patient
87536
HIV viral load test
86359
T-cells, total count
86360
Absolute CD4/CD8 count with ratio
After February 2010 (Medicare HCPCS) (Laboratory tests)
G0432
Infectious agent antigen detection by enzyme immunoassay (EIA) technique,
qualitative or semi-quantitative, multiple-step method, HIV-1 or HIV-2,
screening (conventional test)
G0433
Infectious agent antigen detection by enzyme-linked immunosorbent assay
(ELISA) technique, antibody, HIV-1 or HIV-2, screening
G0435
Infectious agent antigen detection by rapid antibody test of oral mucosa
transudate, HIV-1 or HIV-2, screening
Source:

http://www.nachc.org/client/2010HIVTestingandICD-9CodingGuideUpdatedFrom2008.pdf

Notes: CD4/CD8 = cluster of differentiation 4/8; CPT = current procedural terminology; DNA =
deoxyribonucleic acid; HCPCS = Health Care Procedural Coding System; HTLV = human T-lymphotropic
virus; ICD-9-CM = International Classification of Diseases, 9th Edition, Clinical Modification; RNA =
ribonucleic acid.

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APPENDIX C
NATIONAL DRUG CODES FOR IDENTIFYING
TREATED PATIENTS WITH HIV INFECTION

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NDC Code

NDC Description

00003196401
00003196501
00003196601
00003196701
00003196801
00003362212
00003362312
00003362412
00003363112
00004024451
00004024515
00004038039
00006022761
00006057062
00006057143
00006057301
00006057318
00006057340
00006057342
00006057354
00006057362
00006057465
00054005221
00056047030
00056047330
00056047492
00056051030
00069080760
00069080860
00074052260
00074194063
00074333330
00074395646
00074395977
00074663322
00074663330
00074679922
00087663241
00087663341
00087667117
00087667217
00087667317
00087667417
00173010793
00173010855
00173010856
00173011318
00173047001

ZERIT 15 MG CAPSULE
ZERIT 20 MG CAPSULE
ZERIT 30 MG CAPSULE
ZERIT 40 MG CAPSULE
ZERIT 1 MG/ML SOLN RECON
REYATAZ 300 MG CAPSULE
REYATAZ 100 MG CAPSULE
REYATAZ 150 MG CAPSULE
REYATAZ 200 MG CAPSULE
INVIRASE 500 MG TABLET
INVIRASE 200 MG CAPSULE
FUZEON 90 MG KIT
ISENTRESS 400 MG TABLET
CRIXIVAN 100 MG CAPSULE
CRIXIVAN 200 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 333 MG CAPSULE
ZIDOVUDINE 300 MG TABLET
SUSTIVA 50 MG CAPSULE
SUSTIVA 100 MG CAPSULE
SUSTIVA 200 MG CAPSULE
SUSTIVA 600 MG TABLET
SELZENTRY 150 MG TABLET
SELZENTRY 300 MG TABLET
KALETRA 100MG-25MG TABLET
NORVIR 80 MG/ML SOLUTION
NORVIR 100 MG TABLET
KALETRA 400-100/5 SOLUTION
KALETRA 133.3-33.3 CAPSULE
NORVIR 100 MG CAPSULE
NORVIR 100 MG CAPSULE
KALETRA 200MG-50MG TABLET
VIDEX FNL10MG/ML SOLN RECON
VIDEX FNL10MG/ML SOLN RECON
VIDEX EC 125 MG CAPSULE DR
VIDEX EC 200 MG CAPSULE DR
VIDEX EC 250 MG CAPSULE DR
VIDEX EC 400 MG CAPSULE DR
RETROVIR 10 MG/ML VIAL
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 10 MG/ML SYRUP
EPIVIR 150 MG TABLET

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NDC Code

NDC Description

00173047100
00173050100
00173059500
00173059502
00173066100
00173066101
00173066400
00173067900
00173068700
00173069100
00173071400
00173072100
00173072700
00173074200
00378504091
00378504191
00378504291
00378504391
00378610691
00378888693
00378888793
00378888893
00378888993
00555058801
00555058901
00555059001
00597000201
00597000302
00597004660
00597004724
15584010101
16590006106
16590006110
16590006418
16590006430
16590006460
16590006490
173010793
173010855
173010856
173011318
173047001
173047100
173050100
173059500
173059502
173066100
173066101

EPIVIR 10 MG/ML SOLUTION
RETROVIR 300 MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
ZIAGEN 300 MG TABLET
ZIAGEN 300 MG TABLET
ZIAGEN 20 MG/ML SOLUTION
AGENERASE 50 MG CAPSULE
AGENERASE 15 MG/ML SOLUTION
TRIZIVIR 150-300MG TABLET
EPIVIR 300 MG TABLET
LEXIVA 700 MG TABLET
LEXIVA 50 MG/ML ORAL SUSP
EPZICOM 600-300MG TABLET
STAVUDINE 15 MG CAPSULE
STAVUDINE 20 MG CAPSULE
STAVUDINE 30 MG CAPSULE
STAVUDINE 40 MG CAPSULE
ZIDOVUDINE 300 MG TABLET
DIDANOSINE 125 MG CAPSULE DR
DIDANOSINE 200 MG CAPSULE DR
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
DIDANOSINE 200 MG CAPSULE DR
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
APTIVUS 100 MG/ML SOLUTION
APTIVUS 250 MG CAPSULE
VIRAMUNE 200 MG TABLET
VIRAMUNE 50 MG/5 ML ORAL SUSP
ATRIPLA 600-200MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
RETROVIR 10 MG/ML VIAL
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 10 MG/ML SYRUP
EPIVIR 150 MG TABLET
EPIVIR 10 MG/ML SOLUTION
RETROVIR 300 MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
ZIAGEN 300 MG TABLET
ZIAGEN 300 MG TABLET

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NDC Code

NDC Description

173066400
173067200
173067900
173068700
173069100
173069120
173071400
173072100
173072700
173074200
21695036212
21695036618
21695036706
21695036918
21695084606
23490708706
31722050960
31722051560
31722051660
31722051760
31722051860
3196401
3196501
3196601
3196701
3196801
3362212
3362312
3362412
3363112
35356006406
35356006430
35356006530
35356006624
35356006706
35356006760
35356006806
35356006860
35356006990
35356007006
35356007030
35356007106
35356007160
35356007224
35356007306
35356007330
35356007460
35356007506

ZIAGEN 20 MG/ML SOLUTION
AGENERASE 150MG CAPSULE
AGENERASE 50 MG CAPSULE
AGENERASE 15 MG/ML SOLUTION
TRIZIVIR 150-300MG TABLET
TRIZIVIR 150-300MG TABLET
EPIVIR 300 MG TABLET
LEXIVA 700 MG TABLET
LEXIVA 50 MG/ML ORAL SUSP
EPZICOM 600-300MG TABLET
KALETRA 200MG-50MG TABLET
CRIXIVAN 400 MG CAPSULE
EPIVIR 150 MG TABLET
ZIDOVUDINE 300 MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
ZIDOVUDINE 300 MG TABLET
STAVUDINE 15 MG CAPSULE
STAVUDINE 20 MG CAPSULE
STAVUDINE 30 MG CAPSULE
STAVUDINE 40 MG CAPSULE
ZERIT 15 MG CAPSULE
ZERIT 20 MG CAPSULE
ZERIT 30 MG CAPSULE
ZERIT 40 MG CAPSULE
ZERIT 1 MG/ML SOLN RECON
REYATAZ 300 MG CAPSULE
REYATAZ 100 MG CAPSULE
REYATAZ 150 MG CAPSULE
REYATAZ 200 MG CAPSULE
ATRIPLA 600-200MG TABLET
ATRIPLA 600-200MG TABLET
EPIVIR 300 MG TABLET
EPIVIR 10 MG/ML SOLUTION
LEXIVA 700 MG TABLET
LEXIVA 700 MG TABLET
REYATAZ 150 MG CAPSULE
REYATAZ 150 MG CAPSULE
SUSTIVA 200 MG CAPSULE
TRUVADA 200-300MG TABLET
TRUVADA 200-300MG TABLET
VIRAMUNE 200 MG TABLET
VIRAMUNE 200 MG TABLET
VIRAMUNE 50 MG/5 ML ORAL SUSP
VIREAD 300 MG TABLET
VIREAD 300 MG TABLET
ZERIT 40 MG CAPSULE
ZIAGEN 300 MG TABLET

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NDC Code

NDC Description

35356007560
35356010906
35356010930
35356011006
35356011060
35356011160
35356011201
35356011230
35356011301
35356011330
35356011406
35356011430
35356011506
35356011530
35356011606
35356011660
35356011701
35356013830
35356013918
35356013960
35356018630
35356020530
35356020660
35356020760
35356020860
35356020960
35356025930
35356028460
35356028560
378504091
378504191
378504291
378504391
378610691
4022001
4022101
4024451
4024515
4024648
4038039
49999006206
49999006210
49999006260
49999011906
49999011960
49999038618
49999043103
50962045010

ZIAGEN 300 MG TABLET
EPZICOM 600-300MG TABLET
EPZICOM 600-300MG TABLET
ISENTRESS 400 MG TABLET
ISENTRESS 400 MG TABLET
KALETRA 100MG-25MG TABLET
KALETRA 200MG-50MG TABLET
KALETRA 200MG-50MG TABLET
PREZISTA 300 MG TABLET
PREZISTA 300 MG TABLET
REYATAZ 300 MG CAPSULE
REYATAZ 300 MG CAPSULE
SUSTIVA 600 MG TABLET
SUSTIVA 600 MG TABLET
TRIZIVIR 150-300MG TABLET
TRIZIVIR 150-300MG TABLET
VIRACEPT 625 MG TABLET
NORVIR 100 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
VIDEX EC 400 MG CAPSULE DR
EMTRIVA 200 MG CAPSULE
FUZEON 90 MG KIT
REYATAZ 200 MG CAPSULE
SELZENTRY 150 MG TABLET
SELZENTRY 300 MG TABLET
DIDANOSINE 400 MG CAPSULE DR
PREZISTA 600 MG TABLET
ZERIT 30 MG CAPSULE
STAVUDINE 15 MG CAPSULE
STAVUDINE 20 MG CAPSULE
STAVUDINE 30 MG CAPSULE
STAVUDINE 40 MG CAPSULE
ZIDOVUDINE 300 MG TABLET
HIVID 0.375MG TABLET
HIVID 0.750MG TABLET
INVIRASE 500 MG TABLET
INVIRASE 200 MG CAPSULE
FORTOVASE 200MG CAPSULE
FUZEON 90 MG KIT
COMBIVIR 150-300 MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
RETROVIR 100 MG CAPSULE
VIRACEPT 250 MG TABLET
RETROVIR 10MG/ML SYRUP

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NDC Code

NDC Description

50962045205
51129299902
52959028930
52959038706
52959050712
52959050718
52959050724
52959050730
52959050802
52959050804
52959050806
52959050808
52959050814
52959050815
52959050860
52959050906
52959050912
52959050918
52959050920
52959050924
52959050928
52959050930
52959054602
52959054603
52959054604
52959054606
52959054608
52959054610
52959054614
52959054615
52959054620
52959054628
52959096812
52959096903
54005221
54390558
54464721
54464725
54569177200
54569177201
54569177202
54569177203
54569177204
54569177205
54569365700
54569387700
54569387701
54569397100

UNKNOWN
DIDANOSINE 400 MG CAPSULE DR
VIRACEPT 250 MG TABLET
RETROVIR 300 MG TABLET
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
KALETRA 100MG-25MG TABLET
TRUVADA 200-300MG TABLET
ZIDOVUDINE 300 MG TABLET
VIRAMUNE 50MG/5ML ORAL SUSP
VIRAMUNE 200MG TABLET
VIRAMUNE 200MG TABLET
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
VIDEX 100MG TAB CHEW
HIVID 0.750MG TABLET
HIVID 0.750MG TABLET
VIDEX 150MG TAB CHEW

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NDC Code

NDC Description

54569405300
54569405400
54569405401
54569422100
54569422101
54569422102
54569424200
54569424201
54569424202
54569424203
54569431300
54569431301
54569433300
54569433400
54569433500
54569448500
54569451400
54569452400
54569452401
54569452402
54569452403
54569453800
54569454300
54569454301
54569454302
54569454303
54569454304
54569454305
54569454306
54569456100
54569456101
54569456200
54569456300
54569456301
54569461100
54569461300
54569479200
54569481300
54569488300
54569490500
54569512200
54569514200
54569517600
54569519100
54569533400
54569537400
54569538700
54569539000

ZERIT 30 MG CAPSULE
ZERIT 40 MG CAPSULE
ZERIT 40MG CAPSULE
EPIVIR 150 MG TABLET
EPIVIR 150MG TABLET
EPIVIR 150MG TABLET
INVIRASE 200MG CAPSULE
INVIRASE 200MG CAPSULE
INVIRASE 200MG CAPSULE
INVIRASE 200MG CAPSULE
VIDEX 100MG TAB CHEW
VIDEX 100MG TAB CHEW
EPIVIR 10 MG/ML SOLUTION
RETROVIR 10MG/ML SYRUP
NORVIR 100MG CAPSULE
HIVID 0.375MG TABLET
VIDEX FNL10MG/ML SOLN RECON
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
RETROVIR 300 MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250 MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250MG TABLET
VIRAMUNE 200 MG TABLET
VIRAMUNE 200MG TABLET
RESCRIPTOR 100MG TAB DISPER
FORTOVASE 200MG CAPSULE
FORTOVASE 200MG CAPSULE
SUSTIVA 200 MG CAPSULE
NORVIR 80MG/ML SOLUTION
NORVIR 100MG CAPSULE
AGENERASE 150MG CAPSULE
ZIAGEN 300 MG TABLET
VIDEX 200MG TAB CHEW
RESCRIPTOR 200MG TABLET
KALETRA 133.3-33.3 CAPSULE
VIDEX EC 400 MG CAPSULE DR
TRIZIVIR 150-300MG TABLET
VIREAD 300 MG TABLET
SUSTIVA 600 MG TABLET
ZERIT 1MG/ML SOLN RECON
ZIAGEN 20 MG/ML SOLUTION

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NDC Code

NDC Description

54569541200
54569548000
54569550100
54569550400
54569552100
54569552500
54569553000
54569553200
54569555000
54569558800
54569559400
54569560200
54569564200
54569564300
54569565600
54569566400
54569575200
54569578100
54569580500
54569581400
54569603400
54569614300
54569615900
54569617000
54569617100
54569862000
54569862001
54864725
54868011700
54868197400
54868197402
54868197403
54868249901
54868250001
54868250002
54868250200
54868250401
54868335200
54868335201
54868335300
54868336000
54868344800
54868369300
54868369302
54868369900
54868369901
54868369902
54868378200

ZERIT 15MG CAPSULE
ZERIT 20 MG CAPSULE
EPIVIR 300 MG TABLET
VIDEX EC 250 MG CAPSULE DR
EMTRIVA 200 MG CAPSULE
KALETRA 100-400/5 SOLUTION
REYATAZ 150 MG CAPSULE
REYATAZ 200 MG CAPSULE
LEXIVA 700 MG TABLET
TRUVADA 200-300MG TABLET
EPZICOM 600-300MG TABLET
RESCRIPTOR 200 MG TABLET
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
NORVIR 100 MG CAPSULE
INVIRASE 500 MG TABLET
KALETRA 200MG-50MG TABLET
FUZEON 90 MG KIT
ATRIPLA 600-200MG TABLET
PREZISTA 300 MG TABLET
ISENTRESS 400 MG TABLET
SELZENTRY 150 MG TABLET
PREZISTA 400 MG TABLET
NORVIR 100 MG TABLET
ZIDOVUDINE 300 MG TABLET
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400MG CAPSULE
VIRAMUNE 200MG TABLET
ISENTRESS 400 MG TABLET
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
HIVID 0.375MG TABLET
HIVID 0.750MG TABLET
HIVID 0.750MG TABLET
VIDEX 100 MG TAB CHEW
RETROVIR 10 MG/ML SYRUP
ZERIT 40 MG CAPSULE
ZERIT 40 MG CAPSULE
ZERIT 20 MG CAPSULE
ZERIT 15 MG CAPSULE
ZERIT 30 MG CAPSULE
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
INVIRASE 200 MG CAPSULE
INVIRASE 200 MG CAPSULE
INVIRASE 200 MG CAPSULE
NORVIR 100MG CAPSULE

43

HIV Clinician Workforce Study

Mathematica Policy Research/The Lewin Group

NDC Code

NDC Description

54868378201
54868378202
54868378203
54868384400
54868384401
54868394700
54868411000
54868411300
54868411400
54868411406
54868452000
54868452200
54868452201
54868452400
54868466600
54868466800
54868466900
54868485300
54868485400
54868485700
54868495400
54868506100
54868514100
54868541600
54868546400
54868556600
54868559500
54868560000
54868563100
54868580900
54868583800
54868586400
54868596900
55045220701
55045348103
55045348201
55045354901
55175449401
55175520706
55175520807
55175520901
55289038904
55289038906
55289038914
55289038920
55289039203
55289047727
55289093118

NORVIR 100 MG CAPSULE
NORVIR 100 MG CAPSULE
NORVIR 100 MG CAPSULE
VIRAMUNE 200 MG TABLET
VIRAMUNE 200 MG TABLET
VIRACEPT 250 MG TABLET
FORTOVASE 200MG CAPSULE
CRIXIVAN 400 MG CAPSULE
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
RESCRIPTOR 200 MG TABLET
ZIAGEN 300 MG TABLET
ZIAGEN 300 MG TABLET
KALETRA 133.3-33.3 CAPSULE
VIDEX EC 400 MG CAPSULE DR
SUSTIVA 600 MG TABLET
VIREAD 300 MG TABLET
EMTRIVA 200 MG CAPSULE
REYATAZ 200 MG CAPSULE
REYATAZ 150 MG CAPSULE
LEXIVA 700 MG TABLET
VIRACEPT 625 MG TABLET
TRUVADA 200-300MG TABLET
EPIVIR 300 MG TABLET
DIDANOSINE 250 MG CAPSULE DR
KALETRA 200MG-50MG TABLET
VIDEX EC 250 MG CAPSULE DR
EPZICOM 600-300MG TABLET
PREZISTA 300 MG TABLET
SELZENTRY 300 MG TABLET
REYATAZ 300 MG CAPSULE
INTELENCE 100 MG TABLET
PREZISTA 400 MG TABLET
HIVID 0.750MG TABLET
TRUVADA 200-300MG TABLET
KALETRA 200MG-50MG TABLET
ZIDOVUDINE 300 MG TABLET
RETROVIR 100MG CAPSULE
COMBIVIR 150-300MG TABLET
VIRACEPT 250MG TABLET
CRIXIVAN 400MG CAPSULE
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
VIRAMUNE 200 MG TABLET
VIRACEPT 250 MG TABLET
KALETRA 133.3-33.3 CAPSULE

44

HIV Clinician Workforce Study

Mathematica Policy Research/The Lewin Group

NDC Code

NDC Description

55289094712
555058801
555058901
555059001
55887023030
55887023060
55887023090
55887023130
55887023160
55887023190
56047030
56047330
56047492
56051030
58016068900
58016068930
58016068960
58016068990
58016069000
58016069018
58016069030
58016069060
58016069090
58016069800
58016069830
58016069860
58016069890
58016069900
58016069930
58016069960
58016069990
58016079500
58016079530
58016079560
58016079590
58016086400
58016086430
58016086460
58016086490
58864046230
58864046260
58864046293
59676056001
59676056101
59676056201
59676056301
59676056401
59676057001

KALETRA 200MG-50MG TABLET
DIDANOSINE 200 MG CAPSULE DR
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
SUSTIVA 50 MG CAPSULE
SUSTIVA 100 MG CAPSULE
SUSTIVA 200 MG CAPSULE
SUSTIVA 600 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
EPIVIR 300 MG TABLET
EPIVIR 300 MG TABLET
EPIVIR 300 MG TABLET
EPIVIR 300 MG TABLET
RETROVIR 300 MG TABLET
RETROVIR 300 MG TABLET
RETROVIR 300 MG TABLET
RETROVIR 300 MG TABLET
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
RETROVIR 100MG CAPSULE
PREZISTA 300 MG TABLET
PREZISTA 400 MG TABLET
PREZISTA 600 MG TABLET
PREZISTA 75 MG TABLET
PREZISTA 150 MG TABLET
INTELENCE 100 MG TABLET

45

HIV Clinician Workforce Study

Mathematica Policy Research/The Lewin Group

NDC Code

NDC Description

597000201
597000302
597004601
597004660
597004661
597004724
59762119001
59762119101
59762119201
59762119301
59762365001
6022761
6057062
6057142
6057143
6057301
6057318
6057340
6057342
6057354
6057362
6057465
60760001018
60760001063
60760059504
60760059514
61958040101
61958060101
61958060201
61958070101
62584004611
62584004621
62584004811
62584004821
62682104801
63010001027
63010001030
63010001190
63010002036
63010002118
63010002770
63304092060
65862002460
65862004660
65862004760
65862004824
65862010701
65862011160

APTIVUS 100 MG/ML SOLUTION
APTIVUS 250 MG CAPSULE
VIRAMUNE 200MG TABLET
VIRAMUNE 200 MG TABLET
VIRAMUNE 200MG TABLET
VIRAMUNE 50 MG/5 ML ORAL SUSP
STAVUDINE 15 MG CAPSULE
STAVUDINE 20 MG CAPSULE
STAVUDINE 30 MG CAPSULE
STAVUDINE 40 MG CAPSULE
ZIDOVUDINE 300 MG TABLET
ISENTRESS 400 MG TABLET
CRIXIVAN 100 MG CAPSULE
CRIXIVAN 200MG CAPSULE
CRIXIVAN 200 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 400 MG CAPSULE
CRIXIVAN 333 MG CAPSULE
VIRACEPT 250 MG TABLET
VIRACEPT 250 MG TABLET
COMBIVIR 150-300MG TABLET
COMBIVIR 150-300MG TABLET
VIREAD 300 MG TABLET
EMTRIVA 200 MG CAPSULE
EMTRIVA 10 MG/ML SOLUTION
TRUVADA 200-300MG TABLET
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
COMBIVIR 150-300MG TABLET
VIRACEPT 250MG TABLET
VIRACEPT 250 MG TABLET
VIRACEPT 50 MG/G POWDER
RESCRIPTOR 100 MG TAB DISPER
RESCRIPTOR 200 MG TABLET
VIRACEPT 625 MG TABLET
ZIDOVUDINE 300 MG TABLET
ZIDOVUDINE 300 MG TABLET
STAVUDINE 30 MG CAPSULE
STAVUDINE 40 MG CAPSULE
ZIDOVUDINE 10 MG/ML SYRUP
ZIDOVUDINE 100 MG CAPSULE
STAVUDINE 15 MG CAPSULE

46

HIV Clinician Workforce Study

Mathematica Policy Research/The Lewin Group

NDC Code

NDC Description

65862011260
65862031030
65862031130
65862031230
65862031330
66267050906
66267051418
66267051463
67253010910
67253076120
67253096124
67263023060
67263023212
67263025860
67263026030
67263038760
67263040260
67263043460
67263045530
67263045836
67263051401
67263056830
67263059060
68030605901
68030606001
68030606401
68030606501
68030728301
68030728401
68115009006
68258900301
68258902001
68258902101
68258910801
68258912601
68258914201
68258915801
69080760
69080860
74052260
74194063
74333330
74395646
74395977
74663322
74663330
74679922
74949202

STAVUDINE 20 MG CAPSULE
DIDANOSINE 125 MG CAPSULE DR
DIDANOSINE 200 MG CAPSULE DR
DIDANOSINE 250 MG CAPSULE DR
DIDANOSINE 400 MG CAPSULE DR
COMBIVIR 150-300 MG TABLET
VIRACEPT 250 MG TABLET
VIRACEPT 250 MG TABLET
ZIDOVUDINE 100 MG CAPSULE
STAVUDINE 1 MG/ML SOLN RECON
ZIDOVUDINE 10 MG/ML SYRUP
REYATAZ 150 MG CAPSULE
KALETRA 200 MG-50 MG TABLET
EPIVIR 150 MG TABLET
TRUVADA 200-300 MG TABLET
LEXIVA 700 MG TABLET
SELZENTRY 150 MG TABLET
VIRAMUNE 200 MG TABLET
VIREAD 300 MG TABLET
RESCRIPTOR 100 MG TAB DISPER
ZIDOVUDINE 100 MG CAPSULE
SUSTIVA 600 MG TABLET
PREZISTA 600 MG TABLET
RETROVIR 100 MG CAPSULE
EPIVIR 150 MG TABLET
EPIVIR 150 MG TABLET
RETROVIR 100 MG CAPSULE
COMBIVIR 150-300 MG TABLET
VIRACEPT 250 MG TABLET
COMBIVIR 150-300 MG TABLET
VIREAD 300 MG TABLET
SUSTIVA 600 MG TABLET
SUSTIVA 200 MG CAPSULE
EPIVIR 150 MG TABLET
ZERIT 20 MG CAPSULE
REYATAZ 150 MG CAPSULE
TRIZIVIR 150-300 MG TABLET
SELZENTRY 150 MG TABLET
SELZENTRY 300 MG TABLET
KALETRA 100 MG-25 MG TABLET
NORVIR 80 MG/ML SOLUTION
NORVIR 100 MG TABLET
KALETRA 400-100/5 SOLUTION
KALETRA 133.3-33.3 CAPSULE
NORVIR 100 MG CAPSULE
NORVIR 100 MG CAPSULE
KALETRA 200 MG-50 MG TABLET
NORVIR 100 MG CAPSULE

47

HIV Clinician Workforce Study

Mathematica Policy Research/The Lewin Group

NDC Code

NDC Description

74949254
81010793
81010855
81010856
81011318
87661443
87661543
87661643
87661743
87662443
87662643
87662743
87662843
87663241
87663341
87665001
87665101
87665201
87665301
87666515
87667117
87667217
87667317
87667417
93553006
9376103
9757601

NORVIR 100 MG CAPSULE
RETROVIR IV 10 MG/ML VIAL
RETROVIR 100 MG CAPSULE
RETROVIR 100 MG CAPSULE
RETROVIR 10 MG/ML SYRUP
VIDEX 100MG PACKET
VIDEX 167MG PACKET
VIDEX 250MG PACKET
UNKNOWN
VIDEX 50MG TAB CHEW
VIDEX 150MG TAB CHEW
VIDEX 100MG TAB CHEW
VIDEX 25MG TAB CHEW
VIDEX FNL10 MG/ML SOLN RECON
VIDEX FNL10 MG/ML SOLN RECON
VIDEX 25MG TAB CHEW
VIDEX 50MG TAB CHEW
VIDEX 100MG TAB CHEW
VIDEX 150MG TAB CHEW
VIDEX 200MG TAB CHEW
VIDEX EC 125 MG CAPSULE DR
VIDEX EC 200 MG CAPSULE DR
VIDEX EC 250 MG CAPSULE DR
VIDEX EC 400 MG CAPSULE DR
ZIDOVUDINE 300 MG TABLET
RESCRIPTOR 100 MG TABLET
RESCRIPTOR 200 MG TABLET

48