Challenges in defining an optimal approach to formula-based allocations of public health funds in the United States.

Abstract

Controversy and debate can arise whenever public health agencies determine how program funds should be allocated among constituent jurisdictions. Two common strategies for making such allocations are expert review of competitive applications and the use of funding formulas. Despite widespread use of funding formulas by public health agencies in the United States, formula allocation strategies in public health have been subject to relatively little formal scrutiny, with the notable exception of the attention focused on formula funding of HIV care programs. To inform debates and deliberations in the selection of a formula-based approach, we summarize key challenges to formula-based funding, based on prior reviews of federal programs in the United States.
The primary challenge lies in identifying data sources and formula calculation methods that both reflect and serve program objectives, with or without adjustments for variations in the cost of delivering services, the availability of local resources, capacity, or performance. Simplicity and transparency are major advantages of formula-based allocations, but these advantages can be offset if formula-based allocations are perceived to under- or over-fund some jurisdictions, which may result from how guaranteed minimum funding levels are set or from "hold-harmless" provisions intended to blunt the effects of changes in formula design or random variations in source data. While fairness is considered an advantage of formula-based allocations, the design of a formula may implicitly reflect unquestioned values concerning equity versus equivalence in setting funding policies. Whether or how past or projected trends are taken into account can also have substantial impacts on allocations.
Insufficient attention has been focused on how the approach to designing funding formulas in public health should differ for treatment or service versus prevention programs. Further evaluations of formula-based versus competitive allocation methods are needed to promote the optimal use of public health funds. In the meantime, those who use formula-based strategies to allocate funds should be familiar with the nuances of this approach.

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BioMed Central
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BMC Public Health
Open Access
Review
Challenges in defining an optimal approach to formula-based
allocations of public health funds in the United States
James W Buehler*†1 and David R Holtgrave†2
Address: 1Department of Epidemiology and Center for Public Health Preparedness and Research, Rollins School of Public Health, Emory
University, Atlanta, Georgia, USA and 2Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins
University, Baltimore, Maryland, USA
Email: James W Buehler* - jbuehle@sph.emory.edu; David R Holtgrave - dholtgrave@jhsph.edu
* Corresponding author †Equal contributors
Abstract
Background: Controversy and debate can arise whenever public health agencies determine how
program funds should be allocated among constituent jurisdictions. Two common strategies for
making such allocations are expert review of competitive applications and the use of funding
formulas. Despite widespread use of funding formulas by public health agencies in the United States,
formula allocation strategies in public health have been subject to relatively little formal scrutiny,
with the notable exception of the attention focused on formula funding of HIV care programs. To
inform debates and deliberations in the selection of a formula-based approach, we summarize key
challenges to formula-based funding, based on prior reviews of federal programs in the United
States.
Discussion: The primary challenge lies in identifying data sources and formula calculation methods
that both reflect and serve program objectives, with or without adjustments for variations in the
cost of delivering services, the availability of local resources, capacity, or performance. Simplicity
and transparency are major advantages of formula-based allocations, but these advantages can be
offset if formula-based allocations are perceived to under- or over-fund some jurisdictions, which
may result from how guaranteed minimum funding levels are set or from "hold-harmless"
provisions intended to blunt the effects of changes in formula design or random variations in source
data. While fairness is considered an advantage of formula-based allocations, the design of a formula
may implicitly reflect unquestioned values concerning equity versus equivalence in setting funding
policies. Whether or how past or projected trends are taken into account can also have substantial
impacts on allocations.
Summary: Insufficient attention has been focused on how the approach to designing funding
formulas in public health should differ for treatment or service versus prevention programs.
Further evaluations of formula-based versus competitive allocation methods are needed to
promote the optimal use of public health funds. In the meantime, those who use formula-based
strategies to allocate funds should be familiar with the nuances of this approach.
Published: 29 March 2007
BMC Public Health 2007, 7:44doi:10.1186/1471-2458-7-44
Received: 3 November 2006
Accepted: 29 March 2007
This article is available from: http://www.biomedcentral.com/1471-2458/7/44
© 2007 Buehler and Holtgrave; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Background
In 2004, the legislature in state of Louisiana proposed leg-
islation that would mandate the following approach for
the regional allocation of funds for public health services
related to addictive disorders, developmental disabilities,
and mental health. In the proposed legislation, program
managers were charged to:
[in the first year of implementation] calculate formu-
las to adjust raw population data (by age) to account
for variations in the incidence/prevalence of disabili-
ties and to adjust for variations in estimated demand
for public sector services within...jurisdictions...The
formulas shall be based on the most recent reliable sci-
entific information available related to prevalence and
demand for services and revised and updated as new
research data becomes available... [and in the second
year of implementation to] develop budget allocation
formulas that incorporate population adjusted per
capita funding levels, service access, and service utili-
zation patterns.... The funding formula may also take
in account historical funding levels, urban/rural differ-
ences in service delivery models and costs, funds
reserved for state level administration and planning
and state discretionary grants, and such other factors
as determined applicable by the offices. When inequi-
ties in resource deployment or service access are iden-
tified in the regional profiles, then the allocation
formulas must include incremental strategies to
increase equity of access to services... [1].
This mandate exemplifies the expectations of political
leaders for assuring that public funds are allocated accord-
ing to need and in ways that support the goals of specific
public health programs. Developing a formula that fulfills
this mandate would be daunting and raises multiple ques-
tions: are timely and accurate data available that reflect
the various dimensions of this mandate, how should spe-
cific indicators be combined and weighted mathemati-
cally, how should inequities in resource use or equity in
access to services be defined? If the mix of considerations
that shape allocations is too complex or nuanced, then
attempting to allocate funds using a formula may not be
the optimal approach; and inviting jurisdictions to submit
funding requests and justifications that are reviewed by an
independent panel of experts may be preferable. State and
national public health agencies in the United States
employ a combination of these two strategies to allocate
program funds [2].
Controversy and debate can arise whenever public health
agencies determine how finite program funds should be
allocated among constituent jurisdictions, given the
potential for "losers and winners" whenever alternative
allocation strategies are considered. Under formula-based
methods, the proportion of available resources allocated
to each jurisdiction is determined by a mathematical cal-
culation. Under competitive methods, allocations are
based on expert review of applications. Competitive
awards may be based on a de facto hybrid of qualitative
and formulaic indicators, such as quantitative measures of
morbidity or mortality used by applicants to justify pro-
posed budgets. The merits of these formula-based versus
competitive allocation approaches mirror one another.
Formula-based allocations are generally simpler and more
transparent, while competitive allocations are better
geared to considering the complexities of local circum-
stances. While the allocation of huge sums is at stake
when national and state-level public health programs are
considered, there has been relatively little formal evalua-
tion of formula allocation methods used by federal public
health programs [2], with the notable exception of the
Ryan White HIV CARE program, which has been the sub-
ject of an in-depth assessment by the Institute of Medicine
and multiple reviews by the Government Accountability
Office and others [3-7]. The purpose of this article is to
inform debates regarding the use of formula-based fund-
ing methods.
Discussion
National Academy of Sciences Panel on Formula
Allocations
Formula-based funding is used widely by multiple federal
government agencies in the United States. Prompted ini-
tially by controversies surrounding education program
funding, in 2000, the National Academy of Sciences con-
vened a Panel on Formula Allocations, which issued
reports in 2001 and 2003 [8,9]. The panel reported that in
fiscal years 1999 and 2000, the federal government dis-
tributed over $250 billion using funding formulas. Med-
icaid, a healthcare insurance program for those who meet
certain need or disability criteria, accounted for nearly
half of this spending; followed by highway construction,
education, and various social service programs. The panel
reviewed the elements of formulas, including data sources
and statistical methods, as well as the advantages, limits,
and unintended consequences of formula allocation
methods. The panel concluded its deliberations by recom-
mending that the federal government's Office of Manage-
ment and Budget establish a standing "Committee on
Formula Allocations" to develop "improved simulation
and quality-control techniques for use in formula design"
and to prepare a handbook for legislators and program
managers "that would serve as an introduction to under-
lying concepts and practical considerations in the use of
formula-based fund allocation." [9]. To date, no such
office has been established [2].
The NAS panel observed that funding formulas range
from simple to complex, with simple formulas involving

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a single data source and complex formulas involving mul-
tiple data sources and adjustments for differences in the
cost of providing services or the availability of local
resources [8]. Formulas typically include a minimum
funding level guaranteed to all recipients, with or without
an eligibility threshold, a funding ceiling, and a "hold-
harmless" provision to blunt effects of random fluctua-
tions in source data. The panel concluded that transpar-
ency and perceived fairness were major advantages of
formula-based allocations. These advantages can be offset
if the design and legislative approval of formulas is exces-
sively influenced by advocates from particular jurisdic-
tions, if data sources and calculations imperfectly reflect
program goals, if the use of a data source leads to anoma-
lies in how data are collected (especially when funded
jurisdictions are responsible for collecting the data), if
perceived fairness is undermined by substantial variations
in "per capita" or "per eligible" funding due to guaranteed
minimums or hold-harmless provisions, or if a lack of
clear definitions and procedures creates an appearance of
gaming to suit particular jurisdictions. The NAS panel did
not substantially consider the potentially unique chal-
lenges inherent in funding public health programs aimed
at disease prevention. For example, if disease counts are
used as the basis for funding a prevention program, an
area that is successful in preventing illness could lose
funding, threatening its ability to sustain its success. A
classic illustration of the phenomenon of program success
leading to a decrease in morbidity and a decrease in fund-
ing is the erosion of the infrastructure for tuberculosis pre-
vention and control that occurred in the United States in
1970's and early 1980's, only to be followed by a resur-
gence in tuberculosis in the late 1980s and early 1990s
[10,11]. Recognition of this resurgence prompted
renewed support for tuberculosis programs, which ena-
bled successful efforts to reverse unfavorable trends.[11]
Ryan White HIV CARE program formula
Among formulas used by federal public health agencies in
the United States, it is not surprising that the formulas
used in the various "titles" or components of the Ryan
White HIV CARE program have been subject to the great-
est scrutiny, given both the role of political advocacy in
shaping HIV care programs and controversies surround-
ing approaches to monitoring the HIV epidemic [7,12-
14]. Evaluations of the Ryan White formula are exten-
sively documented elsewhere [2-7], so we will touch on
just a few points from prior reports that illustrate the chal-
lenges in designing a funding formula. Foremost is the
limitation of the source data relative to program objec-
tives, namely cases of AIDS reported to state health
departments [4]. AIDS represents the late stage of HIV dis-
ease, but the program aims to serve people at all stages
and to encourage early care. This limitation of the source
data prompted Congress, when it reauthorized the Act in
2000, to mandate that HIV data be included in formula
beginning in FY-2007 [4,5]. Moreover, program services
are intended as a "safety net" for those who cannot other-
wise afford care, due to limited or exhausted resources, yet
people reported with AIDS include many who would not
meet the program's financial eligibility criteria [4]. The
formula originally used cumulative numbers of AIDS
reports, which eventually included many who had died,
prompting a shift to counts of people living with AIDS [4].
The "hold-harmless" provision embedded in this change
contributed to substantial variations in "per case" fund-
ing, primarily for one geographic area, San Francisco, rel-
ative to others [4,5]. Without this provision, San
Francisco, an area with a large and relatively early AIDS
epidemic and thus a sizeable number of AIDS deaths,
would have lost a substantial share of funding. With this
provision, San Francisco receives a much higher "per case"
allocation than other areas [4,5]. Costs for providing HIV
care vary among regions of the country, but early efforts to
incorporate a cost adjustment in the design of the formula
bogged down in Congressional deliberations and were
eventually abandoned [4]. Theoretically, a state that is
highly successful in providing early HIV care could
achieve a reduction in AIDS cases and a reduction in its
proportional allocation as a result, although this particu-
lar consideration does not appear to have been a major
concern in prior deliberations regarding the use of AIDS
report data in the formula [2-6].
The Ryan White formula also illustrates the potential haz-
ards of using data collected by grantees. States vary in the
quality and completeness of their AIDS surveillance sys-
tems, and these variations are even greater for HIV report-
ing systems [4,6]. The latter is further complicated by
debates surrounding the use of names in HIV reporting
systems. While the vast majority of states have adopted
name-based HIV reporting systems, a few adhere to the
use of systems that employ anonymous coding proce-
dures. To date, CDC has not endorsed the use of non-
name systems, and thus states that use such systems are at
risk for losing a share of funding, as HIV data are phased
into use in the Ryan White formula [6]. Concern about the
proposed impact of the pending inclusion of HIV report
data in the formula is one reason why the National Alli-
ance of State and Territorial AIDS Directors has proposed
delaying the passage of the Ryan White reauthorization
bill, pending further assessment of the impact of this and
other proposed changes to the program [15].
The use of AIDS surveillance data in the Ryan White for-
mula provides an example of the way that funding formu-
las can affect source data. On January 1,1993, CDC
implemented an expansion of AIDS surveillance criteria.
This change had been deliberated for several years, and
states anticipated its implementation. As a result, there

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was a huge spike in reported AIDS cases in early 1993,
when examined by date cases were reported [16], which
reflected the effect of the definition change and reporting
incentives of the Ryan White program, not underlying
trends in the epidemic (Figure 1a). This also led CDC to
implement complex statistical adjustments to describe
trends in AIDS by date of diagnosis had the definition not
changed (Figure 1b) [16,17]. Lastly, the shift from cumu-
lative numbers of AIDS reports, which included people
who had died, to numbers of people living with AIDS [4],
required consideration of the potential impact on the
reporting of AIDS deaths. AIDS case reporting typically
occurs in multiple steps, including an initial report fol-
lowed by updates as additional information becomes
available about subsequent opportunistic illnesses or
death. A funding formula based on numbers of people liv-
ing with AIDS could be a disincentive for states to assure
that deaths are ascertained and reports updated. CDC
averted this potential effect by using a national-level esti-
mate of AIDS death rates and applying that estimate to all
state AIDS reports [18].
Formulas are not as transparent or value-free as they
appear
Transparency means that an observer can readily deter-
mine how an allocation for a particular area was deter-
mined using a formula. This is true, however, only if the
formula is carefully documented and the documentation
is publicly available. For example, in January 2006, the
U.S. Department of Health and Human Services
announced plans to disperse approximately $100 million
to "accelerate state and local pandemic influenza prepar-
edness efforts" using a formula-based method. A press
release described the formula in the following manner:
"...grants will be awarded to all 50 states, 7 territories,
the Commonwealth of Puerto Rico, and the District of
Columbia. Each state will receive a minimum of
$500,000, with additional allocation of funds by pop-
ulation. In addition to the state grants, funds are being
awarded to New York City, Chicago and Los Angeles
County." [19].
No further information was released about the specifics of
the formula, although a table accompanied the press
release showing how much money each state, territory,
commonwealth, and separately-funded city or county
would receive [19]. From this information alone, it is not
possible to re-create the reported allocations because
reports about the formula do not specify 1) the source of
census data, e.g., 2000 census data or inter-census esti-
mates, with the latter being available on the Bureau of
Census Internet site for more recent years for states than
for other areas), 2) whether state calculations include or
exclude the populations of separately-funded cities,
although the latter can be presumed based on approxi-
mate calculations with available data, or 3) procedures
used to define population-based funding provided to ter-
ritories. Moreover, no rationale is provided for how either
state or territorial baseline levels were determined.
The perception of fairness of formula-based funding is
based in part on the presumption that formulas represent
an objective and evidence-based approach to resource
allocation. While alluring, this perception can obscure the
value-based judgments that necessarily underlie the
design of any formula. This is illustrated by the example
in Figure 2, which shows trends in incident case reports
for a hypothetical disease for two states from 2000 to
2005. In this example, both states have the same number
of incident cases in 2005 but arrived at this point via a
decrease in one state and an increase in the other. These
data are to be used in mid -year 2006 to fund a public
health program that will begin January 2007. If a formula
were based on the number of cases in 2005, the most
recent calendar year for which full-year data are available
when the funding decision is being made, each state
would receive half of available funds. If the formula were
based on the average number of cases for 2000–2005, the
split would be approximately 40% for State A and 60% for
State B, even though a plausible assumption is that by
2007 State A would have more cases than State B if recent
trends predict future near-term trends. The allocation
between states may differ more if it were based on preva-
lence rather than incidence or if projections in incidence
or prevalence were considered. The relevance of these
options for formula design would also differ if the pro-
gram in question provided treatment services for people
with the disease versus prevention services.
Characteristics of national public health program formulas
in the United States
In addition to the Ryan White HIV CARE program, major
national programs from the United States Centers for Dis-
ease Control and Prevention and Health Resources Serv-
ices Administration allocate hundreds of millions of
dollars among states, territories, and selected urban areas
using relatively simple formulas, based on either census
counts, historical funding precedents, or a combination of
these approaches, without adjustments for either the
availability of local resources or inter-state variations in
the cost of delivering services [2]. These include programs
for bioterrorism and public health emergency prepared-
ness, maternal and child health services, and a flexible
"block grant" program that allows states to address health
problems not addressed by more categorical programs. In
addition to formula-allocated funds, each of these pro-
grams sets aside varying proportions of funds for pro-
grams designed to meet specific needs or to allow for
innovation, and these set-aside funds may be awarded

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a – AIDS cases by date of report, United States, 1984–1993 Legend: Number of AIDS cases by quarter year of report, United States, 1984–1993
Figure 1
a – AIDS cases by date of report, United States, 1984–1993 Legend: Number of AIDS cases by quarter year of report, United
States, 1984–1993. AIDS surveillance criteria were modified in 1987[*] and 1993[†]). Source: Centers for Disease Control and
Prevention.[15] b – AIDS cases by date of diagnosis, United States, 1986-1993 Estimated AIDS-opportunistic illness incidence
(represents an estimate of AIDS trends if the surveillance definition had not been revised in 1993), adjusted for delays in
reporting, by quarter year of diagnosis, United States, 1986-1993. Source: Centers for Disease Control and Prevention.[17]