United States General Accounting Office

GAO Report to Congressional Requesters

November 1995 RYAN WHITE CARE

ACT OF 1990

Opportunities to

Enhance Funding

Equity

GAO/HEHS-96-26

GAO United States

General Accounting Office

Washington, D.C. 20548

Health, Education, and

Human Services Division

B-265841 

November 13, 1995

The Honorable Nancy Kassebaum

Chairman, Committee on Labor

 and Human Resources

United States Senate

The Honorable Hank Brown

United States Senate

The human immunodeficiency virus (HIV) epidemic has become one of the

most serious health threats to the American public. The HIV infection rate

is estimated to be as high as 1 in every 250 persons nationwide.

Metropolitan areas are especially affected by HIV with rates as high as 1 in

25. By December 1994, nearly 450,000 people with HIV had been reported to

have progressed to acquired immunodeficiency syndrome (AIDS), and more

than 270,000 had been reported to have died of the disease. Currently, only

one federal program provides funds specifically for medical and support

services to individuals with AIDS and HIV. Established by the Ryan White

Comprehensive AIDS Resources Emergency (CARE) Act of 1990, the

program distributed more than $579 million in fiscal year 1994 to eligible

metropolitan areas (EMA) through title I of the act and to states through

title II.

Citing examples of disparities in per-case funding, you expressed concerns

that the existing title I and II funding formulas may lead to an inequitable

distribution of funds to states and EMAs. For this reason, you asked us to

determine (1) how equitably the existing formulas are distributing title I

and II funds, (2) which factors inhibit the formulas from achieving greater

equity, and (3) what formula changes could improve equity.

To assess the title I and II formulas, we reviewed the enacting legislation

and conducted interviews to examine the basic rationale for the factors

used in the current CARE Act formulas. We compared the funding

distributions resulting from the existing formulas against two widely

recognized equity criteria.

The first criterion—beneficiary equity—considers the degree to which a

formula allocates funds to ensure that EMAs and states are able to purchase

a comparable level of services for their HIV populations. Under this

criterion, dollars would be distributed according to two indicators: (1) the

potential number of people with AIDS (that is, caseload) and (2) the cost of

Page 1 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

providing services. The second criterion—taxpayer equity—considers the

degree to which EMAs and states are able to finance a comparable level of

services with comparable burdens on their taxpayers. This second

standard is broader than the first one. In addition to including the two

indicators used in the first standard (caseload and cost), it uses a measure

of each EMA’s and state’s capacity to fund AIDS and HIV services from its

own resources.1

On the basis of interviews with experts and a review of available literature,

we identified data with which to apply these standards to assess the equity

of the title I and II formulas.2

 We used Centers for Disease Control and

Prevention (CDC) data to develop a proxy measure of people living with

AIDS, Health Care Financing Administration (HCFA) data to measure

differences in service costs, and Department of Treasury data to determine

fiscal capacities. We used regression analysis to determine how closely the

distribution of CARE Act funds reflected our equity standards.

We conducted our work from February 1994 through October 1995 in

accordance with generally accepted government auditing standards.

Results in Brief Although the title I and II funding formulas currently include some factors

used in equity-based formulas, they result in wide variations in per-case

funding for several reasons. The most important is that EMAs’ AIDS cases

are double counted; once when calculating funding for EMA medical

services under title I and again when calculating funding for state medical

and other services under title II. A state without an EMA does not benefit

from double counting and receives substantially less funding than a state

with an EMA.

In addition to double counting, the indicators used to target funds to

high-need states and EMAs are inaccurate and could be improved. For

example, we found evidence of large geographic differences in the cost of

serving AIDS patients, yet neither the title I nor the title II formula takes

these differences into account. Also, the title I formula targets funding to

EMAs on the basis of their cumulative number of reported AIDS cases, yet

over 60 percent of these reported cases have since died. As a result, the

1

See the bibliography for other studies that describe these criteria.

2

The experts we interviewed were officials of the Agency for Health Care Policy and Research, the

Centers for Disease Control and Prevention, the Health Care Financing Administration, and the Health

Resources and Services Administration.

Page 2 GAO/HEHS-96-26 Ryan White Funding Formulas
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oldest EMAs receive the most generous funding, and newly emerging EMAs

with more recent growth in AIDS cases receive less funding.

Finally, both title I and title II attempt to target more funds to states and

EMAs where resources are most strained by the epidemic. However, the

indicators used in both formulas are inadequate. The indicator used in the

title I formula (AIDS incidence rate) does not directly take into account an

EMA’s tax base, which determines the area’s own capacity to provide

services to people with HIV. The opposite is true for the title II formula. A

tax base indicator is used (state per capita income), but the effect of a high

AIDS incidence rate on the tax base is ignored.

Funding equity can be improved by changing the structure of the two

formulas to eliminate the inappropriate double counting of AIDS cases and

by using more appropriate measures of EMA and state funding needs.

Background The Congress enacted the Ryan White CARE Act on August 18, 1990, to

“improve the quality and availability of care for individuals and families

with HIV disease.” The CARE Act makes funds available through four titles to

states, EMAs, and nonprofit entities for developing, organizing,

coordinating, and operating more effective and cost-efficient service

delivery systems. The Health Resources and Services Administration, part

of the Department of Health and Human Services’ U.S. Public Health

Service, administers the program.

Over $579 million in CARE Act funds were appropriated in fiscal year 1994

for services to people with AIDS and HIV. About $326 million (56 percent) of

these funds were appropriated for title I, which provides “emergency

assistance” to EMAs—metropolitan areas disproportionately affected by the

HIV epidemic. Half of title I funds are distributed by formula, and half are

distributed competitively. To be eligible, a metropolitan area must have a

cumulative count of more than 2,000 cases of AIDS since reporting began in

1981 or a cumulative count of AIDS cases that exceeds one-quarter of

1 percent of its population. In fiscal year 1994, there were a total of 34

EMAs in 17 states, the District of Columbia, and Puerto Rico. Since fiscal

year 1991, the number of EMAs has more than doubled.

For title II, $184 million (32 percent of total CARE Act funds) were

appropriated in fiscal year 1994. Title II provides funds to states to

improve the quality, availability, and organization of health care and

support services for people with HIV. Of the title II funds distributed to the

Page 3 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

states in fiscal year 1994, 90 percent were distributed by formula, and

10 percent were distributed competitively through Special Projects of

National Significance.3

The remaining titles—titles IIIb and IV—were funded at about $48 million

(8 percent) and $22 million (4 percent), respectively, in fiscal year 1994.

Title IIIb funds are intended for early intervention programs, and title IV

funds are intended for pediatric AIDS programs. Under both of these titles,

funds are awarded competitively.

The Existing

Formulas Meet

Neither Equity

Criterion

Our examination of the existing title I and II formulas indicates that

neither formula meets the beneficiary and taxpayer equity criteria.

Per-case funding is not systematically related to either EMA or state service

costs or their fiscal capacity. (See app. II for details of our analysis.)

The Title I Formula Does

Not Meet Either Equity

Criterion

The title I formula does not meet the beneficiary equity criterion because

per-case funding is not systematically related to the cost of treating people

with HIV. Specifically, our analysis of fiscal year 1994 funding for EMAs

showed that per-case funding ranged from $818 to $2,663—a difference of

over 200 percent. However, only 10 percent of this variation was related to

cost differences4

—though the cost differences themselves were

significant. As an illustration, the Dallas and Oakland EMAs each received

title I allocations of approximately $1,200 per person with AIDS, but the

cost of providing health care services in Oakland is about 37 percent

higher than in Dallas.

The title I formula also does not meet the taxpayer equity criterion

because, in addition to not being systematically related to cost differences,

EMA grant amounts are not highly related to the EMAs’ fiscal capacity. Our

analysis of fiscal year 1994 funding for all EMAs showed that more than

40 percent of the variation in EMAs’ per-case funding was unrelated to

differences in cost and fiscal capacity. For example, the Dallas and

Oakland EMAs received about the same per-case funding, but Oakland’s

3

Special Projects of National Significance (SPNS) are competitive grants awarded to public and

nonprofit organizations to advance knowledge and skills in the delivery of health and support services

to people with HIV.

4

The two EMAs located in Puerto Rico—Ponce and San Juan—were excluded from this analysis. With

these EMAs included in the analyses, cost differences account for only 2 percent of the variation in

per-case funding.

Page 4 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

funding capacity when measured in terms of its tax base, costs, and

concentration of AIDS cases is about 17 percent lower than that of Dallas.

Combined Title I and II

Funding Meets Neither

Equity Criterion

The distribution of combined title I and II funds across states does not

meet either the beneficiary or the taxpayer equity criterion.5

 Total per-case

funding for California and New York is about 20 percent and 30 percent

above the national average, respectively, while Hawaii, Ohio, and Vermont

have total per-case funding levels about 50 percent below the national

average. These funding differences are not strongly related to differences

in states’ costs and fiscal capacity to provide services. Our statistical

analysis found that differences in service costs and fiscal capacity account

for 33 percent of these differences in per-case funding.6

 That is, 67 percent

of the variation in state funding per AIDS case is unrelated to states’ funding

needs. (See app. II for details.)

Funding Inequities

Result From the

Structure and

Components of the

Formulas

Several features of the title I and II formulas contribute to the funding

inequities we have identified. Specifically, inequities occur because EMA

cases are counted in both the title I and II formulas, an inappropriate

caseload measure is included in the title I formula, an inappropriate

measure of EMAs’ and states’ fiscal capacity is included in both formulas,

and neither formula includes a measure of EMAs’ and states’ service costs.

(See appendixes for details.)

Double Counting EMA

Cases

Our analysis of differences in states’ per-case funding amounts indicates

that about half of the variation is due to the double counting of EMA cases

in both the title I and II formulas rather than differences in funding needs

(that is, cost or fiscal capacity differences). States where most cases live in

EMAs receive the largest amounts per case, since larger proportions of their

caseloads are double counted. For example, per-case funding was about

$1,100 in states without an EMA, $1,700 in states where less than half the

state’s caseload lived in an EMA, and $2,200 in states where more than half

of the caseload lived in an EMA (see fig. 1). Thus, most of the variation in

per-case funding can be explained by the extent to which a state’s

caseload is double counted rather than by the state’s funding needs.

5

We compared the total amount of title I and II funds within each state to assess interstate funding

equity. We did not conduct a separate assessment of title II funds because of the difference in the

purposes of these funds between states with and without EMAs.

6

To develop a more valid estimate, we excluded from our analysis those states that received the

minimum title II grant amount of $100,000.

Page 5 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

Figure 1: State Funding by Proportion

of AIDS Cases Residing in an EMA Funding per Case (Dollars)

0

500

1000

1500

2000

0% 1-49% 50% or

More

State AIDS Cases in an EMA (%)

1100

1700

2200

Inappropriate Title I

Caseload Measure

The title I caseload measure is based on the cumulative number of people

with AIDS that EMAs reported to CDC since 1981 when reporting began. By

the end of 1993, however, two-thirds of these people had been reported to

have died and were, therefore, no longer using services funded by title I.

Because the formula includes deceased persons, the EMAs that

experienced the first outbreak of AIDS receive substantially more per-case

funding than do newer EMAs. For example, in fiscal year 1994, the 18 EMAs

that were eligible to receive title I funds in the first 2 years of

eligibility—1991 and 1992—were funded at about $1,500 per case. In

contrast, the 16 EMAs that became eligible in 1993 and 1994 were funded at

only about $1,000 per case—one-third less than the older EMAs (see fig. 2).

Page 6 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

Figure 2: Fiscal Year 1994 Funding Per

Case Funding per Case (Dollars)

0

500

1000

1500

Early

EMAs

Later

EMAs

Note: Early EMAs are the 18 that entered the program during 1991 and 1992; later EMAs are the

16 that entered in 1993 and 1994.

Absence of a Cost Measure While the cost of providing AIDS and HIV services varies among EMAs and

states, neither the title I nor title II formula includes a factor to measure

those differences. Information on the actual costs of providing health and

support services to people with AIDS and HIV within different geographic

areas is not available. However, most of the delivery costs appear to be

associated with the personnel who provide the labor-intensive outpatient

health, support, and case management services titles I and II primarily

fund. A proxy measure for these labor costs is available through the

Medicare Hospital Wage Cost Index.7

Using this index for title I cities, we estimated that the cost of providing

medical services was about 30 percent above the national average in the

New York, Oakland, and San Francisco EMAs and about 10 percent below

the national average in the Miami EMA—a difference of about 40 percent.

This suggests that the New York, Oakland, and San Francisco EMAs must

spend much more than the Miami EMA to provide a comparable level of

services to their patients. Similarly, under title II, we estimated that the

7

The Medicare Hospital Wage Cost Index was designed to reflect personnel costs in hospitals subject

to the Medicare prospective payment system; it was derived from hospital salary surveys.

Page 7 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

cost of providing medical services was more than 15 percent above the

national average in the states of Alaska, California, and New York, about

15 percent below the national average in Alabama and Arkansas, and

about 20 percent below the national average in Mississippi.

Inappropriate Fiscal

Capacity Measures

State and EMA fiscal capacity depends on the size of the tax base and the

service demands placed on that tax base. The current title I formula

measures the demand for services through the use of an AIDS incidence

rate factor, but the strength of each EMA’s tax base is not included. As a

result, the title I formula does not adequately adjust EMAs’ allocations to

target those with smaller tax bases and fewer resources to draw upon to

meet the needs of the cases they must serve.

The title II formula does measure the strength of each state’s tax base

through the use of per capita personal income. However, it does not

consider the demand for services that is placed on state tax bases. As a

result, the title II formula does not adequately adjust state allocations to

target states with tax bases that are burdened by a heavy demand for

services.

Greater Funding

Equity Can Be

Achieved

Greater funding equity can be achieved by changing the formulas’

structure and components. The formulas can be modified to make their

funding distribution meet either the beneficiary equity criterion or the

taxpayer equity criterion. Alternatively, although no formula can

completely satisfy both criteria simultaneously, the formulas could be

modified to partly meet both criteria, emphasizing beneficiary equity over

taxpayer equity or vice versa. Regardless of which criterion is emphasized,

however, the following changes could make the title I and II formulas

more equitable. (See appendixes for details.)

Inequities Created by

Double Counting Could Be

Avoided

The current title I and II structure could be revised to avoid inequities

created by counting EMA cases in both formulas. Presently, funding for

titles I and II does not always reflect the division of service responsibilities

between EMAs and state governments. Through title I, EMAs provide

medical and support services to people who reside in their areas of

coverage. Through title II, states provide medical and support services to

people living outside EMAs and commonly provide these services to people

living in EMAs as well. In addition, through title II, states administer

services such as medication assistance and insurance continuation

Page 8 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

statewide for cases both within and outside of their EMAs.8 Nonetheless,

while EMAs typically provide the bulk of medical services to people living

within their areas, title II provides funding as if states were providing both

medical and statewide services to the EMA cases. This results in a higher

level of per-case funding for states with EMAs because the EMA cases are

double counted.

A more equitable structure would, in effect, double count all cases. Cases

would be counted once for the statewide services such as medication

assistance and insurance continuation, and again for medical services that

are jointly provided by states and EMAs.

One means for achieving this would be to make separate appropriations

for the major activities funded by the CARE Act. One appropriation would

be made for services that state governments provide statewide, and a

second appropriation would be made for medical services that are jointly

provided by states and their EMAs (see fig. 3).9

8

In some cases, EMAs provide funds to the state for the statewide medication assistance program.

9

A third appropriation could be made for discretionary purposes (that is, for competitive grants,

evaluations, and technical assistance).

Page 9 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

Figure 3: Proposed Structure

Congressional

Appropriation

Statewide

Functions

Medical

Services

Competitive Grants,

Evaluation, and

Technical Assistance

Distributed by

Formula

to EMAs

Distributed by

Formula

to States

Distributed by

Formula

to States

Funding for statewide services would be allocated to state governments on

the basis of each state’s total AIDS caseload. Funding for medical services

would be divided into two separate allocations for state governments and

EMAs. The allocation to state governments would be based on AIDS cases

living outside a state’s EMAs. The allocation to EMAs would be based on AIDS

cases living in their service delivery areas. With this method, each state’s

entire caseload is counted twice: once for funding statewide services and

again for funding state-EMA medical services.

The approach would only be a means of allocating federal funds to the

entities responsible for delivering services and would not change the

latitude currently afforded local governments and states in deciding how

to best use those funds. Consequently, this approach should have only a

minimal effect on existing service delivery structures because it leaves EMA

and state responsibilities essentially unchanged.

Page 10 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

Better Indicators of

Caseload, Cost, and

Capacity Would Improve

Equity

In addition to changing the structure of the formulas, funding equity could

be improved by changing the formulas’ components. Specifically, funding

equity could be improved by modifying the existing caseload and fiscal

capacity measures, and by including a cost measure.

First, funding equity could be improved by including a caseload measure

that better reflects the number of people living with AIDS and excludes

deceased persons. We have developed a proxy measure of people living

with AIDS from existing CDC data.

Funding equity could also be improved by including a cost measure, such

as the Medicare Hospital Wage Cost Index. Use of such a measure would

better compensate the EMAs and states that must pay more to provide

services to their patients because of their higher private sector health care

costs.

Finally, to increase resources in states and EMAs with poorer fiscal

capacity, the current fiscal capacity factors could be revised to better

measure the EMAs’ and states’ AIDS incidence rates and tax bases.

Currently, the title I fiscal capacity factor lacks a measure of EMAs’ tax

bases, and the title II factor lacks a measure of states’ AIDS incidence rates.

By having more complete measures of EMA and state fiscal capacities, the

formulas could adjust grants on the basis of both the demand for services

and the strength of tax bases. In addition, using total taxable resources

(TTR) in the state formula instead of personal income could result in a

more comprehensive measure of state tax bases. (For the effects of these

changes on specific state and EMA grants, see app. V.)

Conclusions Our analysis of the existing formulas demonstrates that federal funding

under titles I and II of the CARE Act can be made more equitable. An

important purpose of the Ryan White CARE Act was to target emergency

funding to areas of greatest need. At the time the law was enacted, high

incidences of HIV were found in fewer areas of the country, service delivery

networks were just beginning to form, and these service delivery systems

had to rely primarily on private and volunteer resources. In the past 5

years, however, the HIV epidemic has become more widespread and less

localized. Hence, areas where the AIDS caseload has burgeoned recently

need per-case funding levels comparable to those in areas where AIDS was

initially concentrated.

Page 11 GAO/HEHS-96-26 Ryan White Funding Formulas
B-265841 

Recommendations to

the Congress

To achieve greater equity in the distribution of funds, we recommend that

the Congress modify the funding formulas to

• reduce the double counting of EMA cases so that comparable medical

services funding is available for people with AIDS, regardless of where they

live,

• adopt a caseload indicator that better reflects the number of people living

with AIDS who are in need of services, and

• include an indicator that reflects the relative differences across states and

EMAs in the cost of serving people with AIDS.

Matters for

Congressional

Consideration

If the Congress wishes to target more aid to states and EMAs with limited

fiscal capacity, then it may consider adopting an indicator that reflects the

relative strength of local tax bases and concentrations of people with AIDS.

Alternatively, the Congress may wish to discontinue the use of AIDS

incidence rates in the title I formula and per capita income in the title II

formula because of the funding inequities that these components produce.

Finally, modifying the formulas to achieve a more equitable distribution of

funds will involve significant changes in grants to some EMAs and states.

To avoid possible disruption of service delivery, the Congress may wish to

consider phasing in formula modifications. This should minimize, if not

avoid, disruption for the service delivery networks the CARE Act has made

possible over the last 5 years.

If you or your staff have any questions regarding this report, please

contact me on (202) 512-7119 or Jerry Fastrup, Assistant Director, on

(202) 512-7211. Major contributors to this report are listed in appendix VII.

William J. Scanlon

Associate Director,

Health Financing Issues

Page 12 GAO/HEHS-96-26 Ryan White Funding Formulas
Page 13 GAO/HEHS-96-26 Ryan White Funding Formulas
Contents

Letter 1

Appendix I 

Assessing the Equity

of the Funding

Formulas

18

Existing Formulas 18

Equity Criteria 19

Caseloads 21

Current Caseload Measures Could Be Improved 23

Costs 27

Fiscal Capacity 32

Current Fiscal Capacity Measures Could Be Improved 33

Appendix II 

Title I Funding and

Combined Titles I and

II Funding Meet

Neither Equity

Criterion

38

Equity of Title I Funding 38

Equity of Combined Title I and II Funding 42

Appendix III 

Funding Inequities

Result From the

Structure of the Title I

and II Formulas

46

Double Counting EMA Cases Accounts for Most of the Funding

Inequities

47

Appendix IV 

Structural Changes

Can Improve Equity

50

Consolidated Grants 50

Geographic Division of Service Responsibilities 50

Funding by Types of Service 51

Page 14 GAO/HEHS-96-26 Ryan White Funding Formulas
Contents

Appendix V 

Comparison of EMA

and State CARE Act

Grants Under Current

and Equity-Based

Formulas

54

Beneficiary Equity Formulas 54

Taxpayer Equity Formulas 57

Appendix VI 

Comments From the

Department of Health

and Human Services

62

Appendix VII 

GAO Contacts and

Staff

Acknowledgments

64

Bibliography 65

Tables Table I.1: Cumulative AIDS Survival Rates for a 10-Year Period 23

Table I.2: Title I Caseload Counts 25

Table I.3: Title II Caseload Counts 26

Table I.4: Title I Cost Factor 30

Table I.5: Title II Cost Factor 31

Table I.6: Title I Fiscal Capacity Factors 34

Table I.7: Title II Fiscal Capacity Factors 35

Table V.1: Title I FY 1995 Funding Under the Existing and

Beneficiary Equity Formulas

54

Table V.2: Title II FY 1995 Funding Under the Existing and

Beneficiary Equity Formulas

56

Table V.3: Title I FY 1995 Funding Under the Existing and

Taxpayer Equity Formulas

58

Table V.4: Title II FY 1995 Funding Under the Existing and

Taxpayer Equity Formulas

59

Table V.5: Number of Grantees Experiencing Funding Increases

and Decreases

61

Page 15 GAO/HEHS-96-26 Ryan White Funding Formulas
Contents

Figures Figure 1: State Funding by Proportion of AIDS Cases Residing in

an EMA

6

Figure 2: Fiscal Year 1994 Funding per Case 7

Figure 3: Proposed Structure 10

Figure II.1: Nominal Title I Funding Per Case and Cost 39

Figure II.2: Real Title I Funding Per Case and Tax Base 41

Figure II.3: Nominal Title I and II Per-Case Funding and Cost 43

Figure II.4: Real Title I and II Per-Case Funding and Tax Base 45

Figure III.1: Calculation of Title I Grant 46

Figure III.2: Total Funding 47

Figure III.3: Per-Case Funding 47

Figure III.4: Nominal Combined Title I and II Per-Case Funding

and Percentage of EMA Cases

49

Figure IV.1: Total Grants 52

Figure IV.2: Per-Case Funding 53

Abbreviations

AIDS acquired immunodeficiency syndrome

CARE Comprehensive AIDS Resources Emergency

CDC Centers for Disease Control and Prevention

EMA eligible metropolitan area

GSP Gross State Product

HHS Department of Health and Human Services

HIV human immunodeficiency virus

HCFA Health Care Financing Administration

MHWC Medicare Hospital Wage Cost (Index)

PCI per capita income

PPS prospective payment system

SPNS Special Projects of National Significance

TTR total taxable resources

Page 16 GAO/HEHS-96-26 Ryan White Funding Formulas
Page 17 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

To determine how equitably title I and II funds are distributed, we

examined the existing formulas, applied two widely recognized equity

criteria, and determined whether the existing or alternative formula

factors would best allocate funds according to these standards.

Existing Formulas Title I funds are distributed on the basis of the cumulative number of AIDS

cases EMAs report and their cumulative AIDS incidence rate.

Granti

























Distribution

Factori

i 1

Distribution

Factori

Appropriation

Amount

where

Distribution

Factori

















Casesi

i 1

Casesi













3

REMA

RAll EMAs

In this formula,

Casesi

 = the cumulative number of AIDS cases in the ith EMA,

REMA = the per capita incidence of cumulative AIDS cases in an EMA,

RAll EMAs

 = the per capita incidence of cumulative AIDS cases in all EMAs.

Note: i ranges over all EMAs.

Page 18 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

Title II funds are distributed to states on the basis of the number of AIDS

cases they reported in the 2 most recent fiscal years and their per capita

income.

Granti

























Distribution

Factori

i 1

Distribution

Factori

Appropriation

Amount

where

Distribution

Factori

















Casesi













PCIUS

PCIi

1/3

In this formula,

Casesi

 = the number of cases reported by the ith state in the 2 most recent

fiscal years,

PCI = the average per capita income of the ith state/the United States.

Note: i ranges over all states, the District of Columbia, and territories.

Equity Criteria The two standards of equity that we applied were the beneficiary and

taxpayer equity criteria. To meet the beneficiary equity criterion, funding

should be distributed in a way that enables EMAs and states to purchase

comparable levels of AIDS and HIV medical and support services. In other

words, per-case funding should be about the same in each of the EMAs and

states after adjusting for cost differences.

The formula for producing a funding distribution that meets the

beneficiary equity criterion is

Granti





























Casesi

Cost

Index i

i 1

Casesi

Cost

Index i

A

In this formula,

Page 19 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

Casesi = the number of people in need of services in the ith EMA or state,

Cost Indexi

 = an index measuring relative differences in the per-case cost

of serving recipients in the ith EMA or state,

A = the total amount of funds to be allocated.

Note: i ranges over all EMAs/the District of Columbia, all states and

territories.

To meet the taxpayer equity criterion, funding should be distributed in a

way that enables EMAs and states to purchase comparable levels of AIDS

and HIV services with comparable burdens on their taxpayers. Therefore,

under this criterion, per-case funding should be about the same in each of

the EMAs and states, once adjusted for differences in their service costs

and fiscal capacities. Per-case funding should only differ to the extent that

costs and fiscal capacities do. The formula for producing a funding

distribution that meets the taxpayer equity criterion is

Grant





























Casesi

Cost

Index i

Federal

Percentage i

i 1

Casesi

Cost

Index i

Federal

Percentage i

A

In this formula, cases and costs are the same as in the beneficiary equity

formula and represent an EMA’s or state’s funding need. The federal

percentage represents the share of an EMA’s or state’s funding need that

will be counted in the formula and varies with EMAs’ and states’ fiscal

capacity according to the following formula:10

Federal

Percentagei

1.0 0.20

















Fiscal

Capacity

Index i

The fiscal capacity index represents the ability of grantees to fund services

from state and local resources. We applied a weight of 0.20 to this index

10Equalizing taxpayer burdens requires the mathematical form shown here. See Maternal and Child

Health: Block Grant Funds Should Be Distributed More Equitably (GAO/HRD-92-5, Apr. 2, 1992), pp.

55-62, for a more detailed discussion and derivation of this formula.

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because that is the weight implicitly applied to fiscal capacity through the

AIDS incidence rate found in the existing title I formula.11

As shown in the preceding figures, to meet the beneficiary equity standard,

the funding formula would base its allocation on states’ or EMAs’ cases and

costs, and to meet the taxpayer equity standard, the formula would also

include a fiscal capacity factor. Hence, in determining whether the

formulas distribute title I and II funds in accordance with the beneficiary

and taxpayer equity criteria, we sought indicators that were reflective of

these three factors and were appropriate for use in grant allocation

formulas.

Caseloads We considered four approaches to estimating the number of people living

with AIDS in each of the EMAs and states:

• cumulative AIDS cases,

• AIDS cases less reported deaths,

• AIDS cases reported in the 2 most recent years, and

• weighted AIDS cases.

The first approach—cumulative AIDS cases—is the caseload measure found

in the current title I formula. In the context of our equity criteria, this

approach assumes that the number of people currently living with AIDS can

be estimated by using the cumulative number of AIDS cases reported since

1981. About 66 percent of these AIDS cases are no longer living, however,

and the likelihood of death increases substantially the longer one has AIDS.

As a result, this measure would direct funds more to where the epidemic

occurred initially rather than to where it appeared more recently.

The second approach—AIDS cases less reported deaths—subtracts each

state’s and EMA’s total reported deaths from their total reported AIDS cases

for the 10 most recent years. The total number of living cases is then

determined by adding each year’s surviving cases.

While this approach appears to potentially provide a reasonable estimate

of the number of people living with AIDS, it is not an appropriate caseload

measure for allocating funds. Our interviews with experts and our review

of the literature indicated that this estimate would be biased because

AIDS-related deaths are more extensively and quickly reported in some

11The existing title II fiscal capacity factor does not adjust states’ per capita income by their AIDS

incidence, which results in a perverse allocation of title II funds. Because of this, we did not use the

weight associated with the existing title II fiscal capacity factor.

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states and EMAs than in others, and this results in measurement errors.

Furthermore, since funds are based on the number of people living with

AIDS, those states and EMAs that underreport AIDS-related deaths would be

rewarded, while others with more reliable reporting would, in effect, be

penalized. Many of the experts we interviewed expressed concerns that

this method could introduce incentives to purposely underreport deaths.

Consequently, states and EMAs might delay or not even report these deaths,

which could lead to another bias to the caseload measure and result in less

reliable information on the lifespans of people with the disease.

The third approach that we considered uses the number of AIDS cases

reported in the most recent 2 years to estimate the number of living AIDS

cases. This is the caseload measure currently used in the title II formula,

and it appears to reasonably estimate the number of living cases. However,

because this measure consists of cases from a narrow time frame, we

believe it could be too sensitive to sudden caseload changes and disrupt

the continuity of funding over time. Also, the expected lifespans for people

with AIDS could increase over time. If this occurs in the future, the cases

reported in a 2-year interval may not accurately reflect the number of

people living with the disease.

The final approach—weighted AIDS cases—is a proxy measure of living

AIDS cases. This approach estimates the number of AIDS cases living in an

EMA or state on the basis of on the number of AIDS cases reported to CDC for

each of the most recent 10 years and national average survival rates since

a case was first reported. Specifically, the number of AIDS cases that an EMA

or a state had reported for each of these 10 years would be weighted by

the national percentage of cases estimated to be living as of the first day of

the most recent year of that period.12 These percentages would be

estimated from national data on the number of people reported to have

AIDS during a 10-year period who had not been reported to have died of the

disease.

Table I.1 shows the cumulative survival rates for each of 10 years as of

fiscal year 1992. According to these data, 88 percent of the cases reported

in 1992 were estimated to have survived at least 1 day in that year, and

12Basing our estimate on the number of people who survived as of this date means that it includes the

largest number of people that could potentially need services during the last year of that period. In

contrast, an estimate based on the number of people who had survived the entire last year of that

period would tend to underestimate the population in need of services. This is because people who

had survived part, but not all, of the last year would be omitted from the population even though they

had potentially used services sometime during that last year.

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57 percent of the prior year’s cases were estimated to still be alive as of

that date.

Table I.1: Cumulative AIDS Survival

Rates for a 10-Year Period

Year reported with AIDS

Proportion surviving

into FY 1992

FY 1992 .88

FY 1991 .57

FY 1990 .37

FY 1989 .24

FY 1988 .16

FY 1987 .10

FY 1986 .08

FY 1985 .06

FY 1984 .06

FY 1983 .06

This approach appears to be the most appropriate. Unlike the cumulative

AIDS cases measure, it has been adjusted to account for people with AIDS

who are no longer living and thus better reflects the intended service

population. In contrast to the second approach, this one averages out

differences in reporting mortality and avoids incentives to underreport

deaths. Specifically, since the algorithm for estimating living cases would

be based on national data, any uniqueness in how states and EMAs report

mortality would not affect the amount of funds that they would receive.

Finally, this measure applies differential weights to cases from a wide time

frame. As a result, sudden caseload changes should not significantly

disrupt funding continuity over time. Also, this measure can be adjusted to

recognize changes in AIDS mortality.

Current Caseload

Measures Could Be

Improved

Table I.2 compares the proxy measure of people living with AIDS based on

weighted-AIDS cases and the proxy measure based on cumulative AIDS

cases—the existing title I case measure—for each of the EMAs as of

December 1993. Also shown is each EMA’s caseload share based on these

measures. Since funds are distributed on the basis of caseload shares

rather than number of cases, the former is actually the more relevant

measure from a formula perspective.

The extent to which the cumulative case measure distorts EMAs’ demand

for services is shown by the differences in weighted and cumulative

caseload shares. For example, the cumulative case measure overestimates

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caseload shares for the New York City, San Francisco, Newark, and Jersey

City EMAs from 7.85 to 15.52 percent. Conversely, the cumulative case

measure underestimates demand for services in EMAs such as

Riverside-San Bernardino, Orlando, St. Louis, Tampa-St. Petersburg, and

Phoenix from 13.22 to 18.39 percent. These differences reflect the

distortions created by the large number of deceased persons in the case

counts for the older and larger EMAs.

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Table I.2: Title I Caseload Counts

Weighted cases Cumulative cases

EMA

Count

(#)

Share

(%)

Count

(#)

Share

(%)

Percentage

differencea

New York, NY 23,484 21.46 58,896 23.2 8.12

San Francisco, CA 7,238 6.61 18,107 7.13 7.85

Los Angeles, CA 9,512 8.69 22,710 8.95 2.93

Miami, FL 5,113 4.67 10,970 4.32 –7.51

Newark, NJ 3,246 2.97 8,207 3.23 8.98

Houston, TX 3,948 3.61 9,539 3.76 4.17

Washington, DC 4,376 4.00 10,127 3.99 –0.24

Chicago, IL 4,276 3.91 9,784 3.85 –1.36

Atlanta, GA 3,092 2.83 7,184 2.83 0.17

Fort Lauderdale, FL 2,332 2.13 5,530 2.18 2.25

Philadelphia, PA 3,725 3.40 7,933 3.12 –8.18

Dallas, TX 2,708 2.47 6,088 2.40 –3.09

Boston, MA 3,289 3.01 7,068 2.78 –7.36

Jersey City, NJ 1,194 1.09 3,200 1.26 15.52

San Diego, CA 2,445 2.23 5,298 2.09 –6.60

Oakland, CA 1,899 1.73 4,323 1.70 –1.84

Baltimore, MD 2,510 2.29 5,021 1.98 –13.77

New Orleans, LA 1,282 1.17 3,107 1.22 4.44

Seattle, WA 1,640 1.50 3,702 1.46 –2.67

Tampa-St. Petersburg, FL 2,040 1.86 4,069 1.60 –13.99

Orange County, CA 1,285 1.17 2,966 1.17 –0.51

Nassau-Suffolk, NY 1,453 1.33 3,363 1.32 –0.24

Detroit, MI 1,803 1.65 3,650 1.44 –12.71

West Palm Beach, FL 1,397 1.28 3,188 1.26 –1.59

Denver, CO 1,475 1.35 3,098 1.22 –9.46

Orlando, FL 1,258 1.15 2,391 0.94 –18.09

Riverside-San Bernardino, CA 1,587 1.45 3,004 1.18 –18.39

Bergen-Passaic, NJ 1,113 1.02 2,641 1.04 2.27

Kansas City, MO 1,083 0.99 2,292 0.90 –8.77

New Haven, CT 1,354 1.24 2,855 1.12 –9.08

Phoenix, AZ 1,129 1.03 2,272 0.89 –13.22

St. Louis, MO 1,233 1.13 2,398 0.94 –16.18

San Juan, PR 3,248 2.97 7,291 2.87 –3.23

Ponce, PR 676 0.62 1,595 0.63 1.79

aWhile the data in the table have been rounded to two decimal positions, the percentage

differences were computed based on additional decimal positions.

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Table I.3 compares our proxy measure of people living with AIDS that is

based on weighted living cases and the existing title II case measure—2

years of cases—for each of the states and territories as of December 1993.

The table also shows the caseload shares based on these two measures.

Once again, an examination of caseload shares under these two methods

demonstrates the distortions that result from using only 2 years of cases to

estimate the number of people living with AIDS. The measure overestimates

caseload shares for Delaware and South Dakota from 9.61 to 17.94 percent

and underestimates caseload shares for Hawaii, Montana, and New Jersey

from 5.22 to 7.18 percent.

Table I.3: Title II Caseload Counts

Weighted cases 2 years of cases

State

Cases

(#)

Share

(%)

Cases

(#)

Share

(%)

Percentage

differencea

Alabama 1,164 0.73 1,173 0.76 4.82

Alaska 90 0.06 87 0.06 0.89

Arizona 1,589 0.99 1,621 1.05 6.05

Arkansas 664 0.41 684 0.44 7.16

California 28,299 17.67 27,346 17.76 0.49

Colorado 1,773 1.11 1,732 1.12 1.61

Connecticut 2,375 1.48 2,403 1.56 5.20

Delaware 483 0.30 509 0.33 9.61

District of Columbia 2,429 1.52 2,288 1.49 –2.07

Florida 16,574 10.35 16,007 10.40 0.43

Georgia 4,369 2.73 4,124 2.68 –1.84

Hawaii 556 0.35 496 0.32 –7.18

Idaho 111 0.07 112 0.07 4.55

Illinois 4,960 3.10 4,868 3.16 2.05

Indiana 1,343 0.84 1,353 0.88 4.73

Iowa 305 0.19 313 0.20 6.85

Kansas 524 0.33 546 0.35 8.26

Kentucky 548 0.34 537 0.35 1.99

Louisiana 2,351 1.47 2,218 1.44 –1.88

Maine 207 0.13 193 0.13 –3.27

Maryland 3,735 2.33 3,730 2.42 3.84

Massachusetts 3,692 2.31 3,573 2.32 0.64

Michigan 2,560 1.60 2,564 1.67 4.14

Minnesota 897 0.56 877 0.57 1.71

Mississippi 739 0.46 722 0.47 1.58

Missouri 2,461 1.54 2,455 1.59 3.72

(continued)

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Weighted cases 2 years of cases

State

Cases

(#)

Share

(%)

Cases

(#)

Share

(%)

Percentage

differencea

Montana 58 0.04 53 0.03 –5.55

Nebraska 242 0.15 240 0.16 3.12

Nevada 896 0.56 887 0.58 2.95

New Hampshire 184 0.11 171 0.11 –3.13

New Jersey 8,195 5.12 7,469 4.85 –5.22

New Mexico 414 0.26 401 0.26 0.80

New York 27,593 17.23 25,839 16.78 –2.62

North Carolina 2,019 1.26 1,950 1.27 0.42

North Dakota 20 0.01 20 0.01 1.85

Ohio 2,391 1.49 2,336 1.52 1.57

Oklahoma 962 0.60 989 0.64 6.93

Oregon 1,099 0.69 1,067 0.69 1.00

Pennsylvania 4,660 2.91 4,537 2.95 1.25

Rhode Island 452 0.28 454 0.29 4.44

South Carolina 1,817 1.13 1,869 1.21 6.99

South Dakota 31 0.02 35 0.02 17.94

Tennessee 1,589 0.99 1,609 1.04 5.32

Texas 11,055 6.90 10,452 6.79 –1.68

Utah 408 0.25 399 0.26 1.72

Vermont 96 0.06 100 0.06 8.11

Virginia 2,439 1.52 2,406 1.56 2.60

Washington 2,255 1.41 2,121 1.38 –2.20

West Virginia 175 0.11 160 0.10 –5.13

Wisconsin 947 0.59 960 0.62 5.37

Wyoming 49 0.03 46 0.03 –1.76

Puerto Rico 5,206 3.25 4,812 3.13 –3.88

Virgin Islands 70 0.04 66 0.04 –2.55

aWhile the data in the table have been rounded to two decimal positions, the percentage

differences were computed based on additional decimal positions.

Costs Neither the title I nor title II formula includes a factor that reflects

differences in the cost of serving AIDS cases. We were not able to locate

existing information on the actual cost of providing health and support

services to people with AIDS and HIV within different geographic areas. As a

result, we constructed a proxy for the cost of serving AIDS cases.

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The major factors that typically affect service costs are the personnel who

supply the service, capital costs such as office rent, and supply costs such

as for medications. Titles I and II primarily fund outpatient health, support,

and case management services, which are labor-intensive. Hence, most of

the service delivery costs for services funded by titles I and II would be

associated with the personnel who provide the services.

Furthermore, from our discussions with experts, we determined that an

existing measure of health labor costs—the Medicare Hospital Wage Cost

(MHWC) Index—might be an appropriate indicator of differences in labor

costs among EMAs and states. This wage index was derived by HCFA from

hospital salary surveys and was designed to reflect personnel costs in

hospitals subject to the Medicare prospective payment system (PPS).

Accordingly, the index is based on the salaries of nurses, therapists,

technicians, physicians, and administrative staff. In addition to being used

for PPS, the MHWC Index has been used to estimate cost variation for

ambulatory service centers, home health care providers, and skilled

nursing facilities.

An underlying assumption in our using the MHWC Index to estimate costs

for the personnel who deliver services funded by the CARE Act is that the

relative differences in these costs should mirror the relative differences in

costs of hospital personnel. That is, in places where hospital personnel

costs are high, costs should also be high for the personnel who provide

services funded by the CARE Act. Likewise, in places where hospital

personnel costs are low, the costs for the personnel providing services

funded by the CARE Act should also be low.

HCFA collects nationwide data on hospitals participating in PPS, so cost data

are readily available for each of the EMAs and non-EMA areas. HCFA

publishes these data for metropolitan areas, and using HCFA’s automated

MHWC database, we were able to construct a wage index for each of the

states.

We were unable to locate existing data on the second major cost

category—capital costs. High-cost areas, however, tend to have high costs

both for salaries and capital (for example, rent for office space). In our

view, therefore, the MHWC Index would appear to be a reasonable proxy for

differences in both personnel and capital costs.

The third major cost category—the cost of supplies such as

medications—is assumed not to systematically vary by location. This is

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because the amount an EMA or state pays for supplies like medications is

determined by a number of factors, including the price that they are able

to negotiate with suppliers.

For our analysis, we constructed a cost index assuming 30 percent of costs

do not systematically vary by location and 70 percent do. The MHWC Index

served as a proxy for the variation of these costs.

Cost Index .3 (.7 MHWC)

We applied a weight of 30 percent for costs that do not systematically vary

because that is the approximate percentage of title II funds typically

expended on medications.

Tables I.4 and I.5 display our estimated service costs for each of the EMAs

and states. As shown in these tables, costs can vary by as much as

100 percent. For example, service costs in Oakland are twice those in

Ponce and San Juan, and 48 percent higher than in Miami. Similarly,

service costs in Alaska are over 50 percent higher than in Mississippi.

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Table I.4: Title I Cost Factor

EMA

MHWC Index (average =

1.00)

GAO cost index (.3 + .7 *

MHWC) (average = 1.00)

Oakland, CA 1.47 1.33

New York, NY 1.41 1.29

San Francisco, CA 1.41 1.29

Nassau-Suffolk, NY 1.31 1.21

Los Angeles, CA 1.25 1.18

New Haven, CT 1.23 1.16

San Diego, CA 1.21 1.14

Boston, MA 1.17 1.12

Riverside-San Bernardino, CA 1.17 1.12

Bergen-Passaic, NJ 1.15 1.10

Philadelphia, PA 1.11 1.08

Newark, NJ 1.11 1.08

Jersey City, NJ 1.11 1.08

Washington, DC 1.11 1.08

Seattle, WA 1.10 1.07

Detroit, MI 1.09 1.06

Chicago, IL 1.07 1.05

Fort Lauderdale, FL 1.06 1.04

Denver, CO 1.06 1.04

Atlanta, GA 1.03 1.02

Phoenix, AZ 1.01 1.01

West Palm Beach, FL 1.00 1.00

Orange County, CA 1.00 1.00

Houston, TX 0.99 0.99

Baltimore, MD 0.99 0.99

Dallas, TX 0.95 0.97

New Orleans, LA 0.95 0.96

Kansas City, MO 0.95 0.96

Orlando, FL 0.94 0.96

Tampa-St. Petersburg, FL 0.94 0.96

St. Louis, MO 0.91 0.94

Miami, FL 0.86 0.90

Ponce, PR 0.45 0.62

San Juan, PR 0.44 0.61

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Table I.5: Title II Cost Factor

State

MHWC Index (average =

1.00)

GAO cost index (.3 + .7 *

MHWC) (average = 1.00)

Alaska 1.29 1.20

California 1.25 1.18

New York 1.24 1.17

District of Columbia 1.21 1.15

Connecticut 1.21 1.15

Massachusetts 1.16 1.11

Hawaii 1.13 1.09

New Jersey 1.12 1.08

Nevada 1.11 1.08

Rhode Island 1.09 1.06

Washington 1.05 1.04

Michigan 1.05 1.04

Oregon 1.05 1.03

Delaware 1.03 1.02

New Hampshire 1.02 1.01

Pennsylvania 1.01 1.01

Minnesota 1.00 1.00

Maryland 1.00 1.00

Illinois 0.98 0.99

Arizona 0.98 0.98

Colorado 0.97 0.98

Utah 0.95 0.96

Florida 0.95 0.96

Ohio 0.94 0.96

Vermont 0.93 0.95

Indiana 0.92 0.94

New Mexico 0.91 0.94

Maine 0.91 0.94

Georgia 0.91 0.94

Texas 0.91 0.93

Wisconsin 0.90 0.93

Virginia 0.89 0.92

Nebraska 0.89 0.92

Missouri 0.88 0.92

North Carolina 0.88 0.92

Kansas 0.88 0.91

Louisiana 0.87 0.91

(continued)

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State

MHWC Index (average =

1.00)

GAO cost index (.3 + .7 *

MHWC) (average = 1.00)

Tennessee 0.86 0.91

South Carolina 0.86 0.90

Idaho 0.85 0.90

Montana 0.84 0.89

Kentucky 0.84 0.89

North Dakota 0.84 0.89

West Virginia 0.83 0.88

Iowa 0.83 0.88

Alabama 0.80 0.86

Wyoming 0.80 0.86

Oklahoma 0.80 0.86

South Dakota 0.78 0.84

Arkansas 0.76 0.83

Mississippi 0.70 0.79

Puerto Rico 0.44 0.61

Virgin Islands N/A N/A

Note: N/A = Not applicable.

Fiscal Capacity A comprehensive indicator of an EMA’s or state’s fiscal capacity to provide

AIDS and HIV health and support services is one that includes both a

measure of the resource base (that is, tax base) and the potential demand

placed on these resources to fund AIDS and HIV services.

For the title I formula, we used per capita income (PCI) as the proxy

measure for EMA resources. PCI data are compiled by the Department of

Commerce and are used to measure the income received by a

jurisdiction’s residents, including wages and salaries, rents, dividends,

interest earnings, and income from nonresident corporate business. PCI

also includes an adjustment for the rental value of owner-occupied

housing on the grounds that such ownership is similar to the interest

income earned from alternative financial investments. While PCI does not

measure all taxable income, it is the most comprehensive measure of EMA

residents’ income currently available.

As a proxy for the level of demand placed on EMAs’ resources, we used AIDS

incidence rates based on our estimate of living AIDS cases. AIDS incidence

indicates the proportion of each EMA’s population that has been reported

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to have AIDS. As such, AIDS incidence considers the relative rather than the

absolute demand placed on an EMA’s resources. Those EMAs with larger

proportions of their populations having the disease are expected to have

greater demands on their resources than are EMAs with smaller

proportions of their populations infected.

A complete title I fiscal capacity measure was constructed by first

producing cost-adjusted income amounts for each EMA through dividing

their PCI by their MHWC Index values. This adjustment ensured that EMAs

were compared in terms of income that was of comparable purchasing

power. Next, we divided these cost-adjusted values by each EMA’s AIDS

incidence rate.

Fiscal capacity PCI / MHWC / AIDS Incidence

We followed similar steps in constructing the title II fiscal capacity

measure, with the exception of using total taxable resources (TTR)13 to

measure income. TTR is a broader measure of income than PCI because it

considers all income potentially subject to a state’s taxing authority. TTR is

an average of PCI and per capita Gross State Product (GSP). GSP measures

all income produced or received within a state, whether received by

residents, nonresidents, or retained by business corporations. Below is the

the title II fiscal capacity measure.

Fiscal capacity TTR / MHWC / AIDS Incidence

Current Fiscal

Capacity Measures

Could Be Improved

Under the current formulas, fiscal capacity is incompletely measured. The

title I formula includes a measure of EMAs’ AIDS incidence but omits a

measure of their resources, which creates a bias against those EMAs with

relatively low tax bases. In table I.6, we show EMAs’ fiscal capacity as

measured by the complete indicator that we constructed—real PCI per

weighted case—and by the existing measure—AIDS incidence. This table

also shows the percentage difference or disparity between these two

measures.

As shown in this table, fiscal capacity for the Riverside-San Bernardino

EMA is estimated to be 147 percent of the EMA average when measured with

a complete indicator—PCI per weighted case. When only AIDS incidence is

considered, however, the EMA’s fiscal capacity is estimated to be

245 percent of the average. Hence, when demand for services is

13TTR data are only available at the state level, so they cannot be used to estimate the fiscal capacity of

EMAs.

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considered relative to available resources, Riverside-San Bernardino’s

fiscal capacity is estimated to be 67 percent lower than what is estimated

under the existing formula. Conversely, when measured with a complete

indicator, San Francisco’s fiscal capacity is estimated to be about

21 percent higher than is estimated under the current formula. This occurs

because of the EMA’s relatively high tax base.

Table I.6: Title I Fiscal Capacity

Factors

EMA

GAO’s real PCI

per wtd. case

index

(average = 100)

Current title I

AIDS incidence

indexa

(average = 100)

Percentage

differenceb

New York, NY 41 38 –8.46

San Francisco, CA 29 23 –20.68

Los Angeles, CA 94 104 10.67

Miami, FL 40 48 17.95

Newark, NJ 83 61 –26.16

Houston, TX 106 96 –8.93

Washington, DC 134 112 –16.46

Chicago, IL 218 201 –7.82

Atlanta, GA 118 114 –3.42

Fort Lauderdale, FL 67 61 –8.36

Philadelphia, PA 156 162 4.26

Dallas, TX 129 120 –7.68

Boston, MA 200 209 4.21

Jersey City, NJ 50 45 –9.31

San Diego, CA 102 128 24.75

Oakland, CA 112 129 15.12

Baltimore, MD 119 126 6.40

New Orleans, LA 103 109 6.03

Seattle, WA 168 149 –11.34

Tampa-St. Petersburg, FL 113 135 19.20

Orange County, CA 258 218 –15.33

Nassau-Suffolk, NY 226 204 –9.59

Detroit, MI 265 307 16.10

West Palm Beach, FL 108 74 –31.71

Denver, CO 138 144 4.26

Orlando, FL 108 142 30.96

Riverside-San Bernardino,

CA 147 245 66.79

Bergen-Passaic, NJ 169 127 –24.53

(continued)

Page 34 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

EMA

GAO’s real PCI

per wtd. case

index

(average = 100)

Current title I

AIDS incidence

indexa

(average = 100)

Percentage

differenceb

Kansas City, MO 176 184 4.61

New Haven, CT 167 149 –11.08

Phoenix, AZ 211 267 26.45

St. Louis, MO 255 273 7.01

San Juan, PR 33 67 103.54

Ponce, PR 17 56 236.48

Note: The average values are based on the average for all EMAs.

aA high AIDS incidence rate value reflects a lower fiscal capacity while a high PCI value reflects a

higher fiscal capacity. Consequently, we inverted the AIDS incidence rate to make it comparable

with the other indicator.

bWhile the data in the table have been rounded to two decimal positions, the percentage

differences were computed on the basis of additional decimal positions.

In contrast to the title I formula, the title II formula measures states’

income and omits their AIDS incidence rates. This omission creates a bias

against those states with relatively high service demands on their

resources. Table I.7 shows states’ fiscal capacity when measured by a

complete indicator—real TTR per weighted case—followed by the existing

measure—nominal per capita income. Also, the table shows the

percentage difference or disparity between these two measures.

Table I.7: Title II Fiscal Capacity

Factors

State

GAO’s real TTR

per wtd. case

index 

(average = 100)

Current title II

nominal PCI

index 

(average = 100)

Percentage

differencea

Alabama 208 82 –60.68

Alaska 544 109 –80.00

Arizona 129 86 –33.05

Arkansas 209 78 –62.72

California 64 107 68.81

Colorado 124 101 –18.43

Connecticut 101 136 34.43

Delaware 106 108 1.87

District of Columbia 18 133 618.07

Florida 48 98 103.41

Georgia 96 91 –5.23

(continued)

Page 35 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

State

GAO’s real TTR

per wtd. case

index 

(average = 100)

Current title II

nominal PCI

index 

(average = 100)

Percentage

differencea

Hawaii 134 107 –19.97

Idaho 545 81 –85.13

Illinois 162 109 –32.86

Indiana 257 91 –64.66

Iowa 610 92 –84.88

Kansas 314 98 –68.95

Kentucky 405 83 –79.40

Louisiana 111 79 –28.89

Maine 354 92 –74.01

Maryland 90 116 28.24

Massachusetts 109 121 11.03

Michigan 213 98 –53.74

Minnesota 321 101 –68.54

Mississippi 198 71 –64.18

Missouri 135 95 –29.74

Montana 777 81 –89.58

Nebraska 431 96 –77.67

Nevada 92 102 11.09

New Hampshire 397 116 –70.90

New Jersey 71 133 87.34

New Mexico 199 77 –61.06

New York 42 119 182.77

North Carolina 214 89 –58.48

North Dakota 1,910 85 –95.55

Ohio 283 94 –66.87

Oklahoma 199 82 –58.89

Oregon 147 92 –37.39

Pennsylvania 157 102 –34.97

Rhode Island 129 102 –20.47

South Carolina 113 81 –28.43

South Dakota 1,461 83 –94.29

Tennessee 193 87 –54.66

Texas 101 90 –11.05

Utah 226 77 –65.80

Vermont 362 95 –73.78

Virginia 185 104 –43.83

(continued)

Page 36 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix I 

Assessing the Equity of the Funding

Formulas

State

GAO’s real TTR

per wtd. case

index 

(average = 100)

Current title II

nominal PCI

index 

(average = 100)

Percentage

differencea

Washington 141 103 –26.84

West Virginia 548 76 –86.15

Wisconsin 337 94 –71.99

Wyoming 754 88 –88.35

Puerto Rico N/A N/A N/A

Virgin Islands N/A N/A N/A

Notes: The average values are based on the average for the United States.

N/A = Not applicable.

aWhile the data in the table have been rounded to two decimal positions, the percentage

differences were computed on the basis of additional decimal positions.

As shown in table I.7, Kentucky’s fiscal capacity is estimated to be

17 percent below the national average when only PCI is considered. When

income is adjusted by AIDS incidence rates, however, the state’s fiscal

capacity is estimated to be more than four times the national average. This

occurs because of the relatively low AIDS incidence rate in Kentucky as

compared with the state’s available resources. Conversely, while the

District of Columbia has a relatively large resource base (33 percent above

the national average), its AIDS incidence is also relatively high.

Consequently, when measured with a complete indicator, the District of

Columbia’s fiscal capacity is found to be 72 percent below the national

average.

Page 37 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

We compared the distribution of title I funding and the combined title I

and II funding against the beneficiary and taxpayer equity criteria. These

comparisons indicated that the current formulas do not distribute funding

in accordance with either of the equity criteria.

Equity of Title I

Funding

Under the beneficiary equity standard, the size of the grant award depends

on two factors: the number of cases and the cost of services. If the grant is

expressed on a per-case basis, this implies that per-case funding should

vary only with differences in the cost of services. To determine how well

the current distribution of title I funds meets the beneficiary equity

standard, we performed a regression analysis14 to determine the extent to

which cost differences can account for differences in nominal per-case

funding.15

If the current distribution of title I funds reflected the beneficiary equity

standard, then a substantial share of the variation in per-case funding

could be explained by cost differences. Our statistical analysis, however,

indicates the current distribution of title I funds bears little relation to the

variation in costs. The strength of a relationship is commonly measured by

a statistic known as R2

. In this case, the R2 is 0.10, meaning that

differences in cost account for only 10 percent of the variation in per-case

funding for EMAs. Hence, 90 percent of the variation is related to other

factors.

Examples of the inequities that result from this low R2

 are displayed in

figure II.1. If differences in per-case funding and costs were perfectly

correlated, all EMAs would fall along the straight line shown in this figure.

The wide scatter around the line, however, demonstrates that per-case

funding and costs are not systematically related. For example, the service

costs for the Oakland and San Francisco EMAs are about the same; yet,

per-case funding for San Francisco is about twice that for Oakland. If

per-case funding and costs were more strongly correlated, both EMAs

would be positioned closer to the straight line. Furthermore, Oakland’s

per-case funding would even be slightly higher than San Francisco’s rather

than vice versa.

14Regression analysis is a statistical technique used to measure the degree to which variation in a

variable can be explained by variation in other variables.

15When we refer to nominal dollars or funds, we mean an amount that has not been adjusted for

service cost differences among EMAs and states. In contrast, when we refer to real dollars or funds,

we mean an amount that has been cost-adjusted.

Page 38 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

Figure II.1: Nominal Title I Funding Per Case and Cost

2.0

1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.8 0.9 1.0 1.1 1.2 1.3 1.4

Cost Index

Title I Funding per Wtd. Case Index

Oakland

Dallas Atlanta

West Palm Beach

Denver

District of Columbia

Seattle

Los Angeles

Orlando

Houston

Detroit

Fort Lauderdale

Riverside

Boston

New Haven

San Diego

Baltimore

Tampa

Bergen Kansas City

Philadelphia

Nassau

St. Louis

Orange

Co.

Phoenix Chicago

New York

Miami

Newark

New Orleans

Jersey City

San Francisco

Note: The two EMAs located in Puerto Rico—Ponce and San Juan—are excluded from this figure.

Page 39 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

This relationship is also illustrated by other pairs of EMAs. For example,

New York City’s service costs are about 20 percent higher than Jersey

City’s, but their per-case funding is about the same—about 30 percent

above the EMA average. Consequently, at their current funding levels, the

Jersey City EMA can purchase more services for its patients than can New

York City.

From the perspective of beneficiary equity, therefore, the current per-case

funding distribution is inequitable. However, if these differences can be

accounted for by differences in fiscal capacity, then the grant distribution

may reflect our taxpayer equity criterion.

The taxpayer equity standard implies that per-case funding should be

related to both cost differences and differences in fiscal capacity. Thus, to

determine if the formula meets the taxpayer equity standard, we

performed a regression analysis to determine the extent to which

differences in per-case funding can be explained by both cost and fiscal

capacity differences. We used per-case funding (measured in nominal

dollars) as the dependent variable and used both cost and fiscal capacity

(also measured in nominal dollars) as independent variables.

Our statistical results show that the current distribution of title I funds

does more closely reflect the taxpayer equity standard. The R2

 is 0.56,

meaning that differences in cost and fiscal capacity account for 56 percent

of the variation in EMAs’ per-case funding. Nonetheless, a considerable

amount of unexplained variation in per-case funding—44 percent—

remains.

The tendency to allocate more aid to low-capacity EMAs is shown

graphically in figure II.2. San Francisco’s funding capacity is the lowest of

all the EMAs and its cost-adjusted per-case real funding is high—nearly

40 percent above the average. West Palm Beach has approximately

average funding capacity and receives an average per-case real funding

amount. Detroit, Orange County, and St. Louis have high funding

capacities and receive relatively lower per-case funding amounts. Thus,

there is a clear tendency to target more aid to EMAs with lower funding

capacities.

Page 40 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

Figure II.2: Real Title I Funding Per Case and Tax Base

1.5

1.3

1.1

0.9

0.7

0.5

0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6

Real Tax Base per Wtd. Case

Real Funding per Wtd. Case Index

Oakland

Atlanta

Dallas

West Palm Beach

Denver

District of

Columbia

Seattle

Los Angeles

Orlando

Houston

Fort

Lauderdale

Riverside

Boston

New Haven

San Diego

Baltimore Detroit

Tampa

Bergen

Kansas City

Philadelphia

Nassau

St. Louis

Orange Co.

Phoenix

Chicago

New York

Miami Newark

New Orleans

Jersey City

San Francisco

Note: The two EMAs located in Puerto Rico—Ponce and San Juan—are excluded from this figure.

Page 41 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

However, there are many exceptions to this general tendency. For

example, while the San Francisco and New York City EMAs’ funding

capacities are comparable, they receive very different per-case real

funding amounts. Real per-case funding is 40 percent above the average

for San Francisco and only about average for New York City. Since the

grant amounts have already been adjusted for cost differences, we would

conclude that the New York City EMA is underfunded compared to San

Francisco. Similarly, both West Palm Beach and Tampa have average

funding capacities, but West Palm Beach receives about 25 percent more

title I funds than Tampa. Based on examples like these and our regression

results, we conclude that while title I funding demonstrates a tendency to

target more aid to low-capacity EMAs, substantial inequities exist.

Equity of Combined

Title I and II Funding

The beneficiary equity standard for the combined distribution of title I and

II funds uses caseload and cost measures that encompass the entire state

rather than just an EMA. Using state rather than EMA data, we estimated the

same regression models to assess the equity of the combined title I and II

funding.16

These results show that the current distribution of title I and II funds, in

combination, does not meet either equity standard. If beneficiary equity

were fully realized, cost differences would account for all of the variation

in per-case funding, but costs explain only 14 percent of this variation. To

express this another way, under the beneficiary equity model, states with

the same relative cost of services should receive equal funding on a

per-case basis. As shown in figure II.3, however, states with dramatically

different service costs received comparable per-case funding amounts. For

example, service costs for both Georgia and New Mexico are about

average; but Georgia’s per-case funding is 69 percent higher than New

Mexico’s. A similar situation exists for Ohio and Texas. Both the

regression results and these examples indicate that title I and II funds, in

combination, are not distributed in a way that meets the beneficiary equity

criterion.

16States receiving the minimum title II grant amount of $100,000 are excluded from these analyses

because their funds are not distributed by formula and cannot be related to an equity analysis.

Page 42 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

Figure II.3: Nominal Title I and II Per-Case Funding and Cost

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

0.7 0.8 0.9 1.0 1.1 1.2

Cost Index

Title I and II Funds per Wtd. Case Index

AL

AZ

CA

CT

GA

IL

NY

MA

MD

LA

DE

HI

OK NM

UT

TX

SC TN

RI

PA

OR

NJ

NV

IN

NE

KY

MS

ME

MN

OH

NH

KS

WV WI

WA

VA

VT

NC

MO

MI

ID

IA

DC

FL

CO

AR

Note: States receiving the minimum title II grant amount of $100,000 and the territories are

excluded from this figure.

Page 43 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

Under the taxpayer equity model, cost and fiscal capacity should account

for 100 percent of the variation in per-case funding. Our regression

analysis indicates these two factors account for only 33 percent of the

variation in per-case funding. By implication, about 67 percent of the

variation in per-case funding is unrelated to need as reflected by

differences in cost and fiscal capacity.

Specific examples of the inequities are illustrated in figure II.4. For

example, Massachusetts and New Hampshire receive comparable per-case

funding amounts, but New Hampshire’s tax base is about four times that of

Massachusetts. Similar situations exist for the states of Connecticut and

Kentucky and for Delaware and Maine. In contrast, Hawaii receives a grant

that is about half the amount that Missouri receives; yet, the states’ tax

bases are comparable. This is also the case for the states of Georgia and

Nevada and for Illinois and Oregon. On the basis of this analysis, we

conclude that the combined title I and II funding does not meet the

taxpayer equity criterion.

Page 44 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix II 

Title I Funding and Combined Titles I and II

Funding Meet Neither Equity Criterion

Figure II.4: Real Title I and II Per-Case Funding and Tax Base

1.25

AL

AZ

CA

CT

GA IL NY

MA

MD LA

DE

HI

NM

OK

UT

TX

TN

SC

RI

PA

OR

NJ

NV

IN

KY NE

MS

ME

MN OH

NH

KS

WI WV

WA

VA

VT

NC

MO

MI

ID

IA

DC

FL

CO

AR

1.0

0.75

0.50

0.25

0

0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5

Real TTR per Wtd. Case Index

Real Funds per Wtd. Case Index

Note: States receiving the minimum title II grant amount of $100,000 and the territories are

excluded from this figure.

Page 45 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix III 

Funding Inequities Result From the

Structure of the Title I and II Formulas

When funding for titles I and II is considered jointly, the major cause of

funding inequities is that EMA cases are counted in both formulas but cases

outside EMAs are not. As a result, states with few or no cases in an EMA

receive disproportionately less per-case funding than do states with large

proportions of their caseloads in EMAs.

The following two-state example demonstrates how the current structure

produces funding inequities between a state with an EMA and one without

an EMA. For this example, we will assume that $1,000 has been

appropriated for each of titles I and II. Also, the two states are assumed to

be alike in terms of their costs and funding capacity; however, they differ

in the number of cases they must serve and whether these cases live in an

EMA.

State A has 200 cases, all living in an EMA while State B has 100 cases and

no EMA. Hence, State A has two-thirds and State B has one-third of the total

cases. Since title I funds are allocated based on each state’s share of EMA

cases, the entire $1,000 would be distributed to the EMA in State A, and

none of the funds would be distributed in State B (see fig. III.1).

Figure III.1: Calculation of Title I Grant

Appropriation

$1,000

$1,000

Percent of

EMA Cases

100%

0%

Grant

$1,000

$0

State A

State B

Example:

Formula:

Title II

Title II funding is allocated in proportion to each state’s total caseload.

Since State A has two-thirds of all cases, it would receive two-thirds

($667) of the title II appropriation. State B would receive one-third

($333) of the appropriation.

Each state’s total grant is then determined by summing their title I and II

grants (see fig. III.2).

Page 46 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix III 

Funding Inequities Result From the

Structure of the Title I and II Formulas

Figure III.2: Total Funding

Title I Grant

$1,000

$0

Title II Grant

$667

$333

Total Grant

$1,667

$333

State A

State B

Example:

Formula:

To determine the states’ per-case funding amounts, their total grant

amounts are divided by their total caseloads (see fig. III.3).

Figure III.3: Per-Case Funding

Total Grant

$1,667

$333

Total Cases

200

100

Per-Case

Funding

$8.34

$3.33

State A

State B

Example:

Formula:

In this example, the current title structure produced differences in

per-case funding for these two states that amounts to about 150 percent.

Moreover, this difference is unrelated to the states’ funding needs and

occurs solely because of the existing title structure.

Double Counting EMA

Cases Accounts for

Most of the Funding

Inequities

To determine the extent to which the current structure accounts for

per-case funding differences, we compared two regression models. The

first model was our earlier one that examined the effects of differences in

cost and fiscal capacity on states’ combined title I and II per-case funding

amounts. For the second model, we examined these effects along with the

Page 47 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix III 

Funding Inequities Result From the

Structure of the Title I and II Formulas

effect of the percentage of AIDS cases in an EMA on states’ combined title I

and II per-case funding amounts.

As discussed in appendix II, the cost and fiscal capacity model explains

only 33 percent of the variation in nominal title I and II per-case funding.

In contrast, however, the model that also includes the percentage of EMA

AIDS cases as a factor explains 85 percent of this variation.

The relationship between the percentage of EMA AIDS cases and per-case

funding is displayed in figure III.4. As shown in this figure, the states with

fewer cases in EMAs, for example, Hawaii, Ohio, and West Virginia receive

the smallest grants, and the states with larger percentages of cases in

EMAs, for example, California, the District of Columbia, and New York,

tend to receive the largest grants. As demonstrated by the regression

analysis and this figure, the funding differences result from the structure

of the formulas rather than funding needs as measured by cases, costs, and

fiscal capacity. Consequently, these differences are inequitable.

Page 48 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix III 

Funding Inequities Result From the

Structure of the Title I and II Formulas

Figure III.4: Nominal Combined Title I and II Per-Case Funding and Percentage of EMA Cases

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0

0 10 20 30 40 50 60 70 80 90 100

Percent AIDS in EMAs

Title I and II Funding per Wtd. Case Index

AZ

CA

CT

GA

IL

NY

MA

MD

LA

HI

TX

PA

NJ

OH

NH

KS

WV

WA

VA

MO

MI

DC

FL

CO

Notes: States receiving the minimum title II grant amount of $100,000 and the territories are

excluded from this figure.

Of the states without an EMA, only the two that received the smallest and largest grants are

displayed.

Page 49 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix IV 

Structural Changes Can Improve Equity

While improvements in funding equity can be achieved by adopting better

indicators of caseload, cost, and funding capacity in the allocation

formulas, greater improvement could result from changing the allocation

structures to avoid inequities created by counting EMA cases in both

formulas. A variety of approaches could be used that vary in terms of how

they would affect existing service delivery responsibilities and structures.

Consolidated Grants The simplest way to improve funding equity is to consolidate titles I and II

into a single grant and distribute funds to the state governments through

an equity-based formula. State governments would be the political entity

responsible for using the aid provided for serving those in need. The funds

would be allocated based on each state’s total cases, thus avoiding any

double counting of EMA cases.

Two implications of the consolidated grant approach, however, are the

potential infringement on the autonomy currently afforded EMAs in

delivering services and changes in existing service responsibilities and

structures. Currently, EMAs are responsible for delivering services within

their areas, and they have service delivery networks already in place.

Under a consolidated grant, all funds would be distributed to the states.

Hence, a state could potentially assume total responsibility for service

delivery in EMAs or continue to allow the EMAs to administer the programs

they now operate, funding them from its consolidated grant.

Geographic Division

of Service

Responsibilities

A second corrective approach maintains the two distinct titles—title I for

EMAs and title II for states—however, the EMAs would become responsible

for all services in their areas, including those services currently under the

purview of the states through title II. Hence, EMAs and states would

continue to be funded under separate titles, but the services funded under

these titles would be identical. Both title I and II funds would be allocated

through equity-based formulas. Title I funds would be distributed to EMAs

on the basis of their respective shares of cases, and title II funds would be

distributed to states on the basis of their respective shares of the non-EMA

cases.

Like the previous approach, this one avoids the inequities currently caused

by counting EMA cases in both formulas. Furthermore, as do the existing

formulas, this approach maintains the EMAs’ autonomy in the delivery of

services. In addition, this approach allows comparable per-case funding

levels among the states and EMAs. However, this approach would lead to

Page 50 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix IV 

Structural Changes Can Improve Equity

significant changes in service delivery responsibilities. Because EMAs are

not currently responsible for providing services such as insurance

continuation and medication assistance, they would have to develop the

capacity to administer these services in addition to the medical and

support services they now provide.

Funding by Types of

Service

A third corrective approach involves allocating funds for medical and

support services separately from those services that states provide

statewide. This approach avoids inequities produced by double counting

EMA cases, continues the existing autonomy afforded EMAs, and requires no

changes in existing service delivery responsibilities. Furthermore, the

approach ensures that comparable per-case funding is available across

EMAs and states and between EMA and non-EMA areas.

The following example, using the same two states from the example in

appendix III, illustrates how this approach would improve funding equity.

The two states are alike in terms of their costs and funding capacity, but

they differ in the number of cases they serve and whether the cases live in

an EMA.

In this example, a total of $2,000 is appropriated: $1,500 for medical

services and $500 for statewide services. As will be shown through this

example, dividing funds in this way would result in funding amounts for

title I and II activities that are comparable to what was found in the earlier

example in which both titles each had a $1,000 appropriation. That is,

$1,000 would still be available for the EMA, and $1,000 would still be

available for the states because state funding would be determined by

adding together the $500 for non-EMA medical services and $500 for

statewide services.

State A has 200 cases, all living in an EMA, and State B has 100 cases and no

EMA. Hence, once again, State A has 100 percent of the EMA cases and

two-thirds of total cases. State B has 100 percent of the non-EMA cases and

one-third of total cases. Expressed differently, two-thirds of all cases live

in an EMA and one-third of all cases do not.

Under this approach, the medical services appropriation would be divided

between EMA and non-EMA areas on the basis of their respective caseloads.

Since two-thirds of all cases live in an EMA, two-thirds of the medical

services appropriation ($1,000) would be set aside for the EMA. The

remaining one-third of the medical services appropriation ($500) would be

Page 51 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix IV 

Structural Changes Can Improve Equity

set aside for distribution to states on the basis of the number of cases

living outside an EMA.

EMA medical services funds are allocated based on the shares of cases

living in an EMA. Since State A contains all EMA cases, all of these funds

($1,000) would be allocated within State A. None of these funds would be

allocated within State B as it has no EMA. Non-EMA medical services funds

would be allocated based on states’ shares of cases living outside an EMA.

Since State A has no non-EMA cases, it would receive none of these funds.

State B would receive the entire $500 of non-EMA medical services funds

because it contains all non-EMA cases.

The statewide services appropriation ($500) would be allocated based on

each state’s share of total cases. Since State A has two-thirds of the total

cases, it would receive $334; State B would receive the remaining one-third

of funds ($167).

The states’ total grants would be the sum of their EMA medical services

grant, non-EMA medical services grant, and statewide services grant. In this

case, State A would receive a total of $1,334 and State B would receive a

total of $667 (see fig. IV.1).

Figure IV.1: Total Grants

EMA Medical

Services

Statewide

Services

$1,000

$500

Non-EMA

Medical Services

$334

$167

Total

Grant

$1,334

$0 $667

State A $0

State B

Example:

Formula:

Title II

As before, the states’ per-case funding amounts would be obtained by

dividing their total grant amounts by their total caseloads. Figure IV.2

shows the per-case funding amounts for the two states.

Page 52 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix IV 

Structural Changes Can Improve Equity

Figure IV.2: Per-Case Funding

Total Grant

$1,334

$667

Total Cases

200

100

Per-Case

Funding

$6.67

$6.67

State A

State B

Example:

Formula:

Under our proposed approach, each state would receive identical per-case

funding. This contrasts significantly with the current approach, which

produces highly unequal per-case funding that is unrelated to either costs

or funding capacity and is therefore inequitable.

Page 53 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

In this appendix, we describe how title I and II funding would be

distributed if the formulas were changed to meet either the beneficiary or

taxpayer equity criterion. Both the beneficiary and taxpayer equity

formulas were described in greater detail in appendix I, which also

provided more detailed discussion of the caseload, cost, and fiscal

capacity factors used in these formulas.

Depending on the amount of title I and II funds appropriated or the use of

funding-loss mechanisms such as hold-harmless provisions, formula

modifications could decrease funding to some EMAs and states and

increase funding to others. Whether and how funding losses should be

prevented would be the decision of the Congress; however, in this

appendix, we show the effects of formula changes when title I and II

appropriations remain constant and no funding-loss mechanisms are

employed.

Beneficiary Equity

Formulas

Table V.1 displays each EMA’s title I fiscal year 1995 funding under both the

existing and the beneficiary equity formulas, along with the difference in

funding that would be received under these formulas. Relative to the

existing formula, changes in EMAs’ allocations under the beneficiary equity

formula would range from a decrease of 33.57 percent to an increase of

58.72.

Table V.1: Title I FY 1995 Funding

Under the Existing and Beneficiary

Equity Formulas Difference

EMA

Existing

formula FY

1995 allocation

Beneficiary

equity formula

FY 1995

allocation Dollars Percentage

Atlanta, GA $4,007,435 $4,081,413 $73,978 1.85

Austin, TX 1,085,663 1,288,596 202,933 18.69

Baltimore, MD 2,691,832 3,377,671 685,839 25.48

Bergen-Passaic, NJ 1,452,105 1,605,315 153,210 10.55

Boston, MA 3,456,473 4,830,099 1,373,625 39.74

Chicago, IL 4,924,568 6,270,043 1,345,475 27.32

Dallas, TX 3,385,351 3,472,214 86,863 2.57

Denver, CO 1,668,174 2,043,282 375,107 22.49

Detroit, MI 1,716,243 2,377,827 661,584 38.55

Dutchess County, NY 359,357 391,211 31,854 8.86

Fort Lauderdale, FL 3,635,539 3,318,715 –316,825 –8.71

Houston, TX 5,803,257 5,715,983 –87,273 –1.50

Jacksonville, FL 1,214,884 1,307,390 92,506 7.61

(continued)

Page 54 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Difference

EMA

Existing

formula FY

1995 allocation

Beneficiary

equity formula

FY 1995

allocation Dollars Percentage

Jersey City, NJ 2,406,293 1,759,633 –646,659 –26.87

Kansas City, MO 1,145,290 1,235,732 90,442 7.90

Los Angeles, CA 12,998,478 15,329,003 2,330,525 17.93

Miami, FL 8,079,775 6,585,215 –1,494,560 –18.50

Nassau-Suffolk, NY 1,676,365 2,441,033 764,668 45.61

New Haven, CT 1,484,228 2,143,285 659,057 44.40

New Orleans, LA 1,798,493 1,728,625 –69,868 –3.88

New York, NY 48,636,026 42,538,803 –6,097,223 –12.54

Newark, NJ 5,559,872 4,883,141 –676,731 –12.17

Oakland, CA 2,321,637 3,197,753 876,117 37.74

Orange County, CA 1,490,021 1,841,089 351,068 23.56

Orlando, FL 1,286,590 1,522,576 235,986 18.34

Philadelphia, PA 4,124,036 5,528,896 1,404,860 34.07

Phoenix, AZ 1,096,350 1,332,987 236,637 21.58

Portland, OR 986,510 1,278,214 291,704 29.57

Riverside-San

Bernardino, CA 1,485,035 2,357,024 871,988 58.72

St. Louis, MO 1,137,857 1,385,224 247,367 21.74

San Antonio, TX 960,778 1,119,605 158,827 16.53

San Diego, CA 2,861,916 3,702,743 840,827 29.38

San Francisco, CA 19,126,679 12,705,987 –6,420,692 –33.57

Santa Rosa-Petaluma,

CA 574,580 745,241 170,661 29.70

Seattle, WA 1,920,227 2,430,507 510,280 26.57

Tampa-St. Petersburg,

FL 2,172,534 2,538,261 365,727 16.83

Vineland, NJ 197,896 207,866 9,970 5.04

Washington, DC 5,623,294 6,411,647 788,354 14.02

West Palm Beach, FL 1,961,600 1,849,331 –112,269 –5.72

Caguas, PR 489,261 529,640 40,379 8.25

Ponce, PR 1,020,387 885,680 –134,707 –13.20

San Juan, PR 4,662,110 4,390,498 –271,612 –5.83

Total $174,685,000 $174,685,000

Table V.2 displays the distribution of title II fiscal year 1995 funding under

both the existing and the beneficiary equity formulas. Relative to the

existing formula, changes in states’ allocations under the beneficiary

Page 55 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

equity formula would range from a decrease of 69.84 percent to an

increase of 247.33 percent.17

Table V.2: Title II FY 1995 Funding

Under the Existing and Beneficiary

Equity Formulas Difference

State or territory

Existing

formula FY

1995 allocation

Beneficiary

equity FY 1995

allocation Dollars Percentage

Alabama $1,349,942 $3,175,846 $1,825,904 135.26

Alaska 100,000 337,517 237,517 237.52

Arizona 1,759,313 1,552,919 –206,394 –11.73

Arkansas 753,038 1,649,752 896,714 119.08

California 27,867,193 21,249,330 –6,617,863 –23.75

Colorado 1,980,699 1,381,805 –598,894 –30.24

Connecticut 2,404,858 4,096,894 1,692,036 70.36

Delaware 585,604 1,646,242 1,060,638 181.12

District of Columbia 2,532,524 763,706 –1,768,818 –69.84

Florida 17,780,752 13,052,493 –4,728,259 –26.59

Georgia 4,731,696 4,778,646 46,950 0.99

Hawaii 499,350 1,734,379 1,235,029 247.33

Idaho 138,867 324,283 185,416 133.52

Illinois 5,577,650 2,981,750 –2,595,900 –46.54

Indiana 1,536,770 3,873,203 2,336,433 152.04

Iowa 333,360 792,428 459,068 137.71

Kansas 568,263 903,092 334,829 58.92

Kentucky 643,697 1,618,407 974,710 151.42

Louisiana 2,785,044 3,389,561 604,517 21.71

Maine 228,492 599,407 370,915 162.33

Maryland 4,684,012 1,984,893 –2,699,119 –57.62

Massachusetts 3,776,077 2,777,381 –998,696 –26.45

Michigan 2,675,943 2,904,689 228,746 8.55

Minnesota 973,550 2,649,145 1,675,595 172.11

Mississippi 954,192 1,909,469 955,277 100.11

Missouri 2,504,335 1,680,127 –824,208 –32.91

Montana 100,000 158,978 58,978 58.98

Nebraska 267,083 636,498 369,415 138.31

Nevada 964,174 2,910,751 1,946,577 201.89

New Hampshire 175,763 210,839 35,076 19.96

New Jersey 8,958,831 9,072,667 113,836 1.27

New Mexico 479,074 1,126,763 647,689 135.20

(continued)

17This range excludes Guam.

Page 56 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Difference

State or territory

Existing

formula FY

1995 allocation

Beneficiary

equity FY 1995

allocation Dollars Percentage

New York 29,093,044 16,378,152 –12,714,892 –43.70

North Carolina 2,414,668 6,292,042 3,877,374 160.58

North Dakota 100,000 89,715 –10,285 –10.28

Ohio 2,623,138 7,170,372 4,547,234 173.35

Oklahoma 1,050,786 2,242,488 1,191,702 113.41

Oregon 1,300,587 1,126,733 –173,854 –13.37

Pennsylvania 5,177,510 5,825,048 647,538 12.51

Rhode Island 554,753 1,618,244 1,063,491 191.71

South Carolina 2,679,771 5,747,815 3,068,044 114.49

South Dakota 100,000 89,241 –10,759 –10.76

Tennessee 1,846,877 4,406,616 2,559,739 138.60

Texas 12,636,414 10,282,829 –2,353,585 –18.63

Utah 428,266 1,094,580 666,314 155.58

Vermont 103,727 272,789 169,062 162.99

Virginia 2,642,609 4,722,057 2,079,448 78.69

Washington 2,310,797 2,180,920 –129,877 –5.62

West Virginia 184,768 418,183 233,415 126.33

Wisconsin 1,063,650 2,510,890 1,447,240 136.06

Wyoming 100,000 123,698 23,698 23.70

Guam 2,902 10,411 7,509 258.76

Puerto Rico 7,682,087 3,962,806 –3,719,281 –48.41

Virgin Islands 0 277,015 277,015 N/A

Total $174,766,500 $174,766,500

Note: N/A = Not applicable.

Taxpayer Equity

Formulas

Table V.3 displays title I fiscal year 1995 funding under both the existing

and the taxpayer equity formulas. Relative to the existing formula, changes

in EMAs’ title I allocations under the taxpayer equity formula would range

from a decrease of 37.18 percent to an increase of 38.90 percent.

Page 57 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Table V.3: Title I FY 1995 Funding

Under the Existing and Taxpayer

Equity Formulas Difference

EMA

Existing

formula FY

1995 allocation

Taxpayer

equity FY 1995

allocation Dollars Percentage

Atlanta, GA $4,007,435 $3,875,802 –$131,633 –3.28

Austin, TX 1,085,663 1,316,251 230,588 21.24

Baltimore, MD 2,691,832 3,222,078 530,246 19.70

Bergen-Passaic, NJ 1,452,105 1,296,500 –155,605 –10.72

Boston, MA 3,456,473 3,487,398 30,925 0.89

Chicago, IL 4,924,568 4,520,122 –404,446 –8.21

Dallas, TX 3,385,351 3,219,764 –165,587 –4.89

Denver, CO 1,668,174 1,852,549 184,375 11.05

Detroit, MI 1,716,243 1,223,021 –493,222 –28.74

Dutchess County, NY 359,357 380,235 20,879 5.81

Fort Lauderdale, FL 3,635,539 3,612,284 –23,256 –0.64

Houston, TX 5,803,257 5,798,947 –4,309 –0.07

Jacksonville, FL 1,214,884 1,307,433 92,549 7.62

Jersey City, NJ 2,406,293 1,997,181 –409,111 –17.00

Kansas City, MO 1,145,290 926,488 –218,801 –19.10

Los Angeles, CA 12,998,478 15,608,417 2,609,938 20.08

Miami, FL 8,079,775 7,618,124 –461,651 –5.71

Nassau-Suffolk, NY 1,676,365 1,669,138 –7,226 –0.43

New Haven, CT 1,484,228 1,783,837 299,609 20.19

New Orleans, LA 1,798,493 1,739,789 –58,704 –3.26

New York, NY 48,636,026 49,293,321 657,295 1.35

Newark, NJ 5,559,872 5,153,909 –405,962 –7.30

Oakland, CA 2,321,637 3,041,615 719,978 31.01

Orange County, CA 1,490,021 1,145,238 –344,783 –23.14

Orlando, FL 1,286,590 1,469,051 182,461 14.18

Philadelphia, PA 4,124,036 4,793,757 669,721 16.24

Phoenix, AZ 1,096,350 851,132 –245,218 –22.37

Portland, OR 986,510 997,508 10,999 1.11

Riverside-San

Bernardino, CA 1,485,035 2,062,696 577,660 38.90

St. Louis, MO 1,137,857 714,755 –423,102 –37.18

San Antonio, TX 960,778 974,770 13,992 1.46

San Diego, CA 2,861,916 3,659,542 797,626 27.87

San Francisco, CA 19,126,679 15,050,088 –4,076,591 –21.31

Santa Rosa-Petaluma,

CA 574,580 765,980 191,399 33.31

Seattle, WA 1,920,227 2,065,044 144,817 7.54

(continued)

Page 58 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Difference

EMA

Existing

formula FY

1995 allocation

Taxpayer

equity FY 1995

allocation Dollars Percentage

Tampa-St. Petersburg,

FL 2,172,534 2,432,673 260,139 11.97

Vineland, NJ 197,896 212,618 14,722 7.44

Washington, DC 5,623,294 5,885,978 262,684 4.67

West Palm Beach, FL 1,961,600 1,805,503 –156,098 –7.96

Caguas, PR 489,261 534,078 44,817 9.16

Ponce, PR 1,020,387 893,101 –127,286 –12.47

San Juan, PR 4,662,110 4,427,284 –234,826 –5.04

Total $174,685,000 $174,685,000

Title II funding for fiscal year 1995 under the existing and taxpayer equity

formulas is shown in table V.4. Relative to the existing formula, changes in

states’ allocations under the taxpayer equity formula would range from a

decrease of 77.14 percent to an increase of 290.91 percent.

Table V.4: Title II FY 1995 Funding

Under the Existing and Taxpayer

Equity Formulas Difference

State or territory

Existing

formula FY

1995 allocation

Taxpayer

equity formula

FY 1995

allocation Dollars Percentage

Alabama $1,349,942 $3,419,706 $2,069,764 153.32

Alaska 100,000 270,771 170,771 170.77

Arizona 1,759,313 1,394,264 –365,049 –20.75

Arkansas 753,038 1,761,699 1,008,661 133.95

California 27,867,193 20,848,641 –7,018,552 –25.19

Colorado 1,980,699 1,131,187 –849,512 –42.89

Connecticut 2,404,858 4,397,664 1,992,806 82.87

Delaware 585,604 1,926,550 1,340,946 228.99

District of Columbia 2,532,524 915,525 –1,616,999 –63.85

Florida 17,780,752 14,238,085 –3,542,667 –19.92

Georgia 4,731,696 4,912,075 180,379 3.81

Hawaii 499,350 1,952,008 1,452,658 290.91

Idaho 138,867 260,849 121,982 87.84

Illinois 5,577,650 1,582,396 –3,995,254 –71.63

Indiana 1,536,770 3,990,018 2,453,248 159.64

Iowa 333,360 588,194 254,834 76.44

Kansas 568,263 724,849 156,586 27.56

Kentucky 643,697 1,505,770 862,073 133.93

Louisiana 2,785,044 3,601,976 816,932 29.33

(continued)

Page 59 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Difference

State or territory

Existing

formula FY

1995 allocation

Taxpayer

equity formula

FY 1995

allocation Dollars Percentage

Maine 228,492 568,826 340,334 148.95

Maryland 4,684,012 1,561,736 –3,122,276 –66.66

Massachusetts 3,776,077 2,273,689 –1,502,388 –39.79

Michigan 2,675,943 2,126,388 –549,555 –20.54

Minnesota 973,550 2,547,146 1,573,596 161.63

Mississippi 954,192 2,085,536 1,131,344 118.57

Missouri 2,504,335 1,279,436 –1,224,899 –48.91

Montana 100,000 96,946 –3,054 –3.05

Nebraska 267,083 534,883 267,800 100.27

Nevada 964,174 3,408,013 2,443,839 253.46

New Hampshire 175,763 67,697 –108,066 –61.48

New Jersey 8,958,831 9,696,998 738,167 8.24

New Mexico 479,074 1,195,562 716,488 149.56

New York 29,093,044 16,787,811 12,305,233 –42.30

North Carolina 2,414,668 6,826,133 4,411,465 182.69

North Dakota 100,000 33,846 –66,154 –66.15

Ohio 2,623,138 7,253,179 4,630,041 176.51

Oklahoma 1,050,786 2,373,374 1,322,588 125.87

Oregon 1,300,587 950,165 –350,422 –26.94

Pennsylvania 5,177,510 5,283,271 105,761 2.04

Rhode Island 554,753 1,868,964 1,314,211 236.90

South Carolina 2,679,771 6,725,031 4,045,260 150.96

South Dakota 100,000 22,856 –77,144 –77.14

Tennessee 1,846,877 4,778,344 2,931,467 158.73

Texas 12,636,414 9,992,376 –2,644,038 –20.92

Utah 428,266 1,129,611 701,345 163.76

Vermont 103,727 254,367 150,640 145.23

Virginia 2,642,609 4,808,147 2,165,538 81.95

Washington 2,310,797 1,815,450 –495,347 –21.44

West Virginia 184,768 301,237 116,469 63.04

Wisconsin 1,063,650 2,358,029 1,294,379 121.69

Wyoming 100,000 76,242 –23,758 –23.76

Guam 2,902 10,475 7,573 260.97

Puerto Rico 7,682,087 3,973,781 3,708,306 –48.27

Virgin Islands 0 278,726 278,726 N/A

Total $174,766,500 $174,766,500

(Table notes on next page)

Page 60 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix V 

Comparison of EMA and State CARE Act

Grants Under Current and Equity-Based

Formulas

Note: N/A = Not applicable.

As demonstrated by these tables, changing the existing title I and II

formulas would redistribute funds across EMAs and states. Compared with

the existing formulas, either the beneficiary or the taxpayer equity formula

would increase funding for more EMAs and states than it would decrease it

(see table V.5).

Table V.5: Number of Grantees

Experiencing Funding Increases and

Decreases

Type of formula Increased funding Decreased funding

Title I beneficiary equity 31 11

Title II beneficiary equity 37 16

Title I taxpayer equity 22 20

Title II taxpayer equity 33 20

Nonetheless, under either formula, funding would decrease for some EMAs

and states if appropriations remained stable. A number of mechanisms

could be employed, however, to avoid EMA and state funding losses when

funding equity is improved. Appropriations could be made to a level that

obviates funding losses, hold-harmless provisions could be applied, or a

limit could be placed on the amount of funds eligible for redistribution.

For example, funding losses could be avoided by only redistributing funds

that were appropriated in excess of a previous year’s amount.

Page 61 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix VI 

Comments From the Department of Health

and Human Services

We solicited comments on our report from the Department of Health and

Human Services (HHS) through the Director, Office of HIV/AIDS Policy,

Health Education and Human Services. He provided us his comments,

along with those of the Division of HIV Services, Health Resources Services

Administration, which administers titles I and II of the CARE Act. We also

received comments from officials of the Centers for Disease Control and

Prevention (CDC).

In their general comments, the HHS officials stated that moving to a more

equitable funding formula could cause significant funding changes and

potential disruption to service delivery structures. We share these

concerns and have discussed methods to avoid these kinds of difficulties

in our report.

The HHS officials also raised questions about the appropriateness of the

Medicare Hospital Wage Cost (MHWC) Index as a proxy for estimating labor

costs for AIDS and HIV services among EMAs and states. While we believe a

wage index that is more closely related to these services would be

preferable to the MHWC Index, we were unable to locate such an index. On

the basis of our discussions with experts, however, we determined that the

MHWC Index would be an appropriate alternative to a wage index that is

specific to AIDS and HIV services.

In addition, the HHS officials expressed concerns about the adequacy of

both the level of funding and the health care infrastructure for AIDS and HIV

services. While problems may exist with regard to funding and

infrastructure, these issues were not within the scope of our study. The

HHS officials provided specific comments about our report, which have

been incorporated as appropriate.

The CDC officials indicated that they agreed that a caseload indicator based

on an estimate of living cases was preferable to the existing measure;

however, they recommended the use of the number of AIDS cases reported

during the previous 2 years rather than our proposed measure of weighted

cases. These officials stated that our caseload measure would require

annual revision, would serve as an incentive to states to underreport

AIDS-related mortality, would be technically difficult to compute, and was

not a standard method for estimating living AIDS cases.

We agree that our caseload measure might periodically need revision, as

would any such measure, in accordance with significant changes in AIDS

mortality. However, our caseload measure, when adjusted over time,

Page 62 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix VI 

Comments From the Department of Health

and Human Services

would more appropriately reflect the impact of changes in AIDS mortality

on the number of people living with AIDS than would a measure based on

cases reported in the previous 2 years. We do not believe our proposed

measure would serve as an incentive to underreport AIDS-related mortality

because states’ funding would not be directly affected by their reported

mortality data. As discussed in appendix I, we propose the use of weighted

AIDS cases as a proxy measure rather than an actual estimate of living AIDS

cases to avoid this potential incentive. Finally, we do not believe that our

proposed measure would be technically difficult to compute.

Page 63 GAO/HEHS-96-26 Ryan White Funding Formulas
Appendix VII 

GAO Contacts and Staff Acknowledgments

GAO Contacts Jerry Fastrup, Assistant Director, (202) 512-7211

Greg Dybalski, Senior Economist, (202) 512-7210

Mark Vinkenes, Senior Social Science Analyst, (202) 512-6841

Acknowledgments In addition to those named above, the following individuals made

important contributions to this report: David Bieritz, Evaluator; Leslie

Albin, Reports Analyst; Ann McDermott, Publishing Adviser.

Page 64 GAO/HEHS-96-26 Ryan White Funding Formulas
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