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BMC Health Services Research
Open Access
Research article
HIV prevention costs and program scale: data from the PANCEA
project in five low and middle-income countries
Elliot Marseille*1, Lalit Dandona2, Nell Marshall1, Paul Gaist3,
Sergio Bautista-Arredondo4, Brandi Rollins1, Stefano M Bertozzi4,
Jerry Coovadia5, Joseph Saba6, Dmitry Lioznov7, Jo-Ann Du Plessis5,
Evgeny Krupitsky7, Nicci Stanley5, Mead Over9, Alena Peryshkina10, SG
Prem Kumar11, Sowedi Muyingo12, Christian Pitter8, Mattias Lundberg13 and
JamesGKahn
1
Address: 1Institute of Health Policy Studies, University of California, San Francisco, USA, 2George Institute for International Health – India,
Hyderabad, India; Health Studies Area, Centre for Human Development, Administrative Staff College of India, Hyderabad, India; School of Public
Health and George Institute for International Health, University of Sydney, Sydney, Australia, 3Office of AIDS Research, National Institutes of
Health, Bethesda, USA, 4Instituto Nacional de Salud Pública, Cuernavaca, Mexico, 5HIVAN(Centre for HIV/AIDS Networking), Durban, South
Africa, 6Axios International, Paris, France, 7St. Petersburg Pavlov State Medical University, St. Petersburg, Russia, 8Elizabeth Glaser Pediatric AIDS
Foundation, Washington, D.C., USA, 9Center for Global Development, Washington, D.C., USA, 10AIDS Infoshare, Moscow, Russia, 11George
Institute for International Health – India, Hyderabad, India; Health Studies Area, Centre for Human Development, Administrative Staff College
of India, Hyderabad, India, 12Axios International, Kampala, Uganda and 13World Bank, Washington, D.C., USA
Email: Elliot Marseille* - emarseille@comcast.net; Lalit Dandona - LDandona@george.org.in; Nell Marshall - nell.marshall@ucsf.edu;
Paul Gaist - gaistp@od31em1.od.nih.gov; Sergio Bautista-Arredondo - sbautista@correo.insp.mx;
Brandi Rollins - brandi@healingourchildren.org; Stefano M Bertozzi - sbertozzi@correo.insp.mx; Jerry Coovadia - coovadiah@nu.ac.za;
Joseph Saba - sabaj@axiosint.com; Dmitry Lioznov - lioznov@spmu.rssi.ru; Jo-Ann Du Plessis - maxduplessis@mac.com;
Evgeny Krupitsky - kru@ek3506.spb.edu; Nicci Stanley - stazig@yahoo.com; Mead Over - mover@cgdev.org;
Alena Peryshkina - alena@infoshare.ru; SG Prem Kumar - sg.premkumar@george.org.in; Sowedi Muyingo - muyingos@axiosint.com;
Christian Pitter - cpitter@pedaids.org; Mattias Lundberg - mlundberg@worldbank.org; James G Kahn - jgkahn@itsa.ucsf.edu
* Corresponding author
Abstract
Background: Economic theory and limited empirical data suggest that costs per unit of HIV
prevention program output (unit costs) will initially decrease as small programs expand. Unit costs
may then reach a nadir and start to increase if expansion continues beyond the economically
optimal size. Information on the relationship between scale and unit costs is critical to project the
cost of global HIV prevention efforts and to allocate prevention resources efficiently.
Methods: The "Prevent AIDS: Network for Cost-Effectiveness Analysis" (PANCEA) project
collected 2003 and 2004 cost and output data from 206 HIV prevention programs of six types in
five countries. The association between scale and efficiency for each intervention type was
examined for each country. Our team characterized the direction, shape, and strength of this
association by fitting bivariate regression lines to scatter plots of output levels and unit costs. We
chose the regression forms with the highest explanatory power (R2).
Results: Efficiency increased with scale, across all countries and interventions. This association
varied within intervention and within country, in terms of the range in scale and efficiency, the best
Published: 12 July 2007
BMC Health Services Research 2007, 7:108 doi:10.1186/1472-6963-7-108
Received: 2 April 2007
Accepted: 12 July 2007
This article is available from: http://www.biomedcentral.com/1472-6963/7/108
© 2007 Marseille et al; 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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fitting regression form, and the slope of the regression. The fraction of variation in efficiency
explained by scale ranged from 26% – 96%. Doubling in scale resulted in reductions in unit costs
averaging 34.2% (ranging from 2.4% to 58.0%). Two regression trends, in India, suggested an
inflection point beyond which unit costs increased.
Conclusion: Unit costs decrease with scale across a wide range of service types and volumes.
These country and intervention-specific findings can inform projections of the global cost of scaling
up HIV prevention efforts.
Background
There is wide agreement that an effective response to the
global HIV epidemic requires very substantial resources.
This consensus has been partially translated into increas-
ing contributions to combat the epidemic [1]. Aggregate
commitments by major donors such as the Global Fund
to Fight Malaria, TB and HIV; the U.S. President's Emer-
gency Program for AIDS Relief (PEPFAR); the European
Union; and, the Gates Foundation suggest that we have
entered an era in which the total funds allocated to stem
the HIV epidemic may constitute a significant portion of
the amount needed. Yet the imperative to spend this
money efficiently can hardly be over-stated: the lives of
millions depend upon how effectively available funds are
allocated. Sound resource allocation and program budget-
ing, in turn, must rest on a foundation of robust unit cost
estimates for the most important prevention modalities in
key epidemic and cultural settings.
There is a substantial, growing, but still limited body of
data on the costs of HIV prevention services [2-12]. These
data provide a reasonable basis for estimating service costs
for some intervention types in some settings, particularly
in sub-Saharan Africa. However many other intervention-
setting pairs remain unexamined. In addition to an insuf-
ficient number of cost data points, data have often been
gathered using different data collection instruments and
compiled using different methods. These data are there-
fore not always directly comparable.
Micro-economic theory and empirical evidence suggest
that under ordinary circumstances downward sloping
average total costs flatten out and eventually turn up to
form a U-shaped curve [13]. While the concept of disecon-
omies of scale was originally developed in conjunction
with the theory of the firm [14,15], the causes of scale dis-
economies are not specific to the private sector. These
causes include increasing costs of communication,
increasing worker alienation, bureaucratic inertia, and
duplication of effort. Certain inputs may become more
costly too. For example, at least over the short term, a pro-
gram may exhaust the available supply of lower-wage but
adequately trained staff in the area, and be forced to hire
staff that are more expensive. On the demand side, after
the most willing and accessible clients have been served,
it becomes increasingly expensive to reach and motivate
the next client. Information concerning the threshold
beyond which unit costs increase can help inform plans
for program expansion [16]. For example, they can help to
determine whether it is more efficient to cover a given area
with fewer, but larger HIV prevention facilities, or with a
larger number of smaller facilities.
While cost data are limited, still less is understood about
how HIV program expansion affects costs and effective-
ness. Among the unanswered questions, are: "How rap-
idly do costs decline with scale? How does the strength of
the relationship between unit cost and scale vary by inter-
vention and by country? At what service volume do unit
costs start to rise again?" In the absence of data on scale
effects, efforts to project resource requirements for scaled-
up HIV/AIDS programs assume constant unit costs and
vary this assumption in sensitivity analyses [17,18]. It is
understood that the assumption of constant unit costs
may result in substantial inaccuracies [19]. We are aware
of only one empirical study of the relationship between
scale and unit costs of an HIV prevention program. This
analysis of the effect of scale on total and on unit costs in
17 sex worker programs run by non-governmental organ-
izations in southern India found decreasing unit cost up
to about 1,000 – 1,700 sex workers served annually, after
which unit costs rise in a classic U-shaped curve [20]. In a
2005 review of the costs of scaling up health interventions
in developing countries, the authors found that there were
insufficient data to support their goal of deriving typical
unit cost curves as interventions increase in scale [21].
"Prevent AIDS: Network for Cost-Effectiveness Analysis"
(PANCEA) is a five-country study funded by the U.S.
National Institutes of Health. It has the purpose of provid-
ing essential information and analysis for an improved
allocation of HIV prevention funds in low and middle-
income countries. The study includes five countries: India,
Mexico, Russia, South Africa, and Uganda. The results pre-
sented here draw on PANCEA data to describe the rela-
tionship of program efficiency (unit cost) and scale
(number of units of services delivered). The PANCEA
analysis ultimately aims to measure cost-effectiveness, i.e.,
the cost per "outcome" such as HIV cases averted. The
present study assesses efficiency in the production of "out-
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puts" such as the number of clients served, that are the
proximate cause of behavior change and thus of epidemic
impact.
Methods
PANCEA design and data collection methods have been
detailed previously and are summarized here [22].
Program sample
PANCEA examined multiple interventions in varied
organizational settings and countries. The number of pro-
grams sampled totaled 228, of which 206 are included in
this analysis of unit cost variation by scale (Table 1). Con-
dom social marketing and school program data were
excluded due to low sample size per country (0–2). We
also excluded data on an intervention-country if data were
obtained from fewer than five prevention sites of that type
in the country.
Data on Outputs and Costs
We developed an integrated set of data collection instru-
ments to portray program operations with sufficient detail
and flexibility to capture variation among nominally sim-
ilar program types, and with sufficient standardization to
permit valid comparisons across multiple programs.
PANCEA gathered information on several service outputs
for each intervention type. Among all program services,
the key outputs selected for examination were those
thought to capture program resources as they relate to the
ability to reduce HIV risk behavior and transmission. For
some interventions, the key output is based on a complete
unit of service, e.g., for Voluntary Counseling and Testing
(VCT), this constitutes receiving the counseling and test-
ing sequence of VCT, through post-test counseling. For
other interventions, the key output is based on person-
years of core services, e.g., for IDU risk reduction, receiv-
ing one clean needle a week for a year. For yet other inter-
ventions, a mix of core services is provided and we
measure overall interaction intensity, e.g., for sex worker
programs, the total hours of program client contact. These
key outputs are used to assess program efficiency. They are
listed in Table 2.
We conducted a comprehensive assessment of the costs of
running HIV prevention interventions. Cost instruments
were extensively adapted from templates produced by
others to add a time element and pre-specified inputs spe-
cific to the interventions. The instruments record data on
resources used and expenditures. Cost data were collected
in five standard categories: personnel (clinical and sup-
port), recurrent goods (e.g., test kits), recurrent services
(e.g., utilities), capital (e.g., computers and vehicles), and
building or other operating space (purchase or rental).
The instruments were piloted and revised, and pro-
grammed into Excel and MS Access. We costed all donated
inputs (i.e., employed economic rather than financial
costing), using unit costs for inputs as determined by local
price quotes.
Data collection was co-coordinated by a group at the Uni-
versity of California, San Francisco (UCSF), and con-
ducted in 2003 and 2004 by local HIV research teams.
These teams were selected based on a prior record of high
quality data collection and analysis, ability to assemble an
appropriately skilled and managed team, and network of
contacts among HIV prevention agencies. In-country col-
laborative teams were India (Administrative Staff College
of India), Mexico (Instituto Nationale de Salud Publica),
Russia (Pavlov University and AIDS Infoshare); South
Africa (HIVAN and Axios International), and Uganda
(Axios International). Each team underwent a two-week
training program, including theoretical and practical com-
ponents.
Each HIV prevention program site studied required seven
to twelve person-days of work, including initial and fol-
low-up visits. All data were reviewed by the UCSF team to
identify gaps and inconsistencies, which were resolved
through written and telephonic queries posed to the local
Table 2: Key outputs used for analysis of variation in unit cost by
scale
Intervention Key output
VCT Clients receiving VCT counseling* & testing
SW Hours of contact with program clients
STI First visits for suspected STI
IDU risk reduction Client-year of needles (weekly)
IEC Hours of media exposure
PMTCT Women receiving post-test counseling
* In Russia, VCT counseling is often much briefer than UNAIDS
Guidelines.
Table 1: Number and type of HIV prevention sites utilized in the
analysis of relationship between scale and unit costs
VCT SW STI IEC RR MTCT Total
India 17 15 13 15 60
Mexico 18 6 22 46
Russia – Pavlov 9 6 16 31
Russia – AI 10 10 6 26
So. Africa 14 15 29
Uganda 14 14
All countries 82 40 25 22 22 15 206
Key– VCT: Programs providing voluntary counseling and testing;
SW: Programs targeting sex workers; STI: Programs that focused on
treatment or prevention of sexually transmitted infections; IEC:
Information, education and communication programs; RR: Risk
reduction programs focusing on injection drug users; MTCT:
Programs to reduce mother-to-child-transmission of HIV.
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research teams. Data verification visits were conducted at
10 percent of sites (typically 4 per country) to verify the
accuracy of PANCEA data. Half of the sites were chosen
based on surprising or interesting findings. The other half
were chosen randomly. During these visits, local and
UCSF team members reviewed and resolved potential
problems and all key output and cost data.
We used written project records when available. For exam-
ple, VCT programs usually have detailed records on the
specific intervention steps such as pre-test counseling and
HIV tests administered. However, available documents
were often incomplete or imperfectly matched to PAN-
CEA instruments, and thus required interpretation by
respondents to provide needed data. Without inclusion of
these less formal data sources, many HIV prevention pro-
grams would have been excluded; we were willing to sac-
rifice some precision for a more inclusive portrayal of the
universe of HIV prevention programs. Some programs
showed an imperfect match between the number of HIV
test kits acquired and the number used in a given time
period. In sites that had a greater than 10% discrepancy
between the number reported to have been acquired and
the number of tests administered, we assigned test kit
costs according to the number used augmented by 10% to
account for wastage.
Analysis
The cost of each program is defined as the sum of all
inputs (resources) multiplied by the unit costs for these
inputs. We relied on financial data if a market price was
paid by the program, otherwise on market value. We
report in detail elsewhere on the methods and results for
program total costs and cost per key output, including by
category of cost [10,22] The analysis here focuses on the
relationship of program scale and efficiency (cost per key
output).
Scale is defined as the quantity of key outputs delivered
during a 12-month period (the most recent full fiscal year
available). The key outputs are listed in Table 2. We deter-
mined scale directly from key output data (e.g., number of
individuals receiving VCT services to post-test coun-
seling), or calculated it based on the mix of different serv-
ices and the frequency, average duration, and number of
participants for each (e.g., sex worker programs).
Program cost is defined as the total value of resources used
to deliver program services for the same 12-month time
period. This is "economic cost", i.e., includes financial
expenditures as well as the market value of donated or
subsidized inputs (such as volunteer labor or donated test
kits). Costs in local currencies were converted into US$
based on the average exchange rate in the year for which
data are presented.
Efficiency (or cost per key output) is defined as the pro-
gram cost divided by the scale. There is no adjustment for
potential savings due to HIV infections averted.
We examined the association between scale and efficiency
for all programs of an intervention type within each coun-
try. We characterized the direction, shape, and strength of
association by applying all of the bivariate regression
forms available in MS Excel, which include linear, loga-
rithmic, polynomial, power, and exponential. We chose
and present the regression form which yielded the highest
R-square values. Due to the multiplicity of relationships
examined, and to maintain a focus on scale, we did not
perform multi-variate regressions for this overview analy-
sis. We graphed efficiency versus scale using ordinal and
abscissa scales (linear or logarithmic) that portray the
range of results for each intervention most clearly.
Service delivery quality
Quality of services could explain a portion of the potential
association between unit cost and volume. For the 86 VCT
sites, we therefore performed constructed a regression
model to examine the association between quality indica-
tors and unit cost. The independent variables included
features of service delivery (i.e., duration of pre-test and
post-test counseling sessions, availability of supplies,
length of time clients waited to see a counselor, and the
turn-around time for tests results), management (i.e., use
of formal service delivery protocols, staff training, extent
to which operations were monitored including politeness
to clients, following protocol, use of performance incen-
tives and penalties) and evaluation (i.e., whether an inde-
pendent evaluation had ever been conducted, how often
and for what purposes). The main model also included
dummy variables for country. In an additional model,
unit costs were adjusted for purchasing power parity.
Ethics committee approval
The University of California, San Francisco's Committee
on Human Research, approved this study.
Results
We found that efficiency increased (unit costs decreased)
with scale, across all countries and interventions we exam-
ined. This association varies within intervention and
country, in terms of the observed range in efficiency and
scale, the type of regression equation that provides the
best fit, and slope of the regression line, and the propor-
tion of variation in efficiency explained by scale. Of the 15
country-intervention pairs studied, the regression lines for
two, STI and MTCT programs in India, suggest an inflec-
tion point beyond which we expect to see an up-turn in
unit costs. Of these two, only one, the regression line for
MTCT programs, was statistically significant. With simple
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linear functions, the regression trends were downward
sloping in all cases.
Table 3 summarizes the rate at which unit costs change
when output levels double (from 25th percentile scale)
and the portion of variability in unit costs that are
explained by scale (R2). Both unit cost declines associated
with a doubling of output and R2 vary dramatically
between countries and between interventions. Declines
for VCT programs range from 32.5% in India to 2.4% in
South Africa and R2 values ranged from 0.83 in India to
0.29 in Uganda. The strongest association between
decrease in unit cost and doubling of scale were found in
India and Russia's STI programs, although these associa-
tions were not statistically significant. All other observed
associations were substantial and statistically significant
except for that found in Mexico's VCT sites which had a p-
value of 0.066, just exceeding the usual 0.05 p-value for
statistical significance.
Voluntary Counseling and Testing Programs
For VCT, data from all five countries show strong scale
effects, i.e., sites with higher service volume tend to have
lower unit costs (See Figure 1). Scale varied 100-fold
within countries, and 1,000-fold across the full sample.
Efficiency (cost per person receiving full VCT) varies from
10-fold to more than 100-fold within country, and over
all five countries varies from $668 (in Mexico) down to
$1.50 (in Russia, where counseling may be just a few min-
utes). The proportion of variation explained by scale var-
ied from 20% (Mexico) to 83% (India). In Mexico, each
doubling in scale is associated with a drop of $30 per VCT
client (7–27%, depending on starting point). In South
Africa, the effect of doubling in scale is low until more
than 10,000 VCT clients (with the curve shape driven by
one large program). For India, Uganda, and Russia, a dou-
bling in size is associated with 27% to 32% lower costs.
VCT unit cost and quality
We found no statistically significant relationship between
any of our VCT program quality indicators and unit cost.
The R-square for the full model, which included dummy
variables for countries and adjustments for purchasing
power parity was 0.05, suggesting that quality, as reflected
in our analysis, explains almost none of the variation in
observed unit costs.
Sex Worker Programs
For sex worker (SW) programs, data from three countries
show very strong scale effects (See Figure 2). Scale ranges
100-fold within countries, and more than 1,000-fold
across the sample. Efficiency (cost per hour of contact
with SWs) varies 100-fold within country, and over all
three countries varies from $378 for a program in South
Africa that provided only 692 hours of client contact,
down to $0.04 for a program in Russia with very large
group sessions, and thus many client-hours of contact.
The proportion of variation explained by scale is high:
38% (South Africa), 84% (Russia), and 88% (India). All
regressions were statistically significant. For South Africa,
each doubling in scale is associated with a 31% decrease
in cost per hour. For Russia, there is a $5 decrease per hour
of contact for a doubling in scale. For India, doubling
leads to a 42% drop in cost per hour. The data from India
includes a program that includes large public gatherings.
This generates high service volumes and very low unit
Table 3: Strength of the relationship and coefficient of determination between unit cost and program scale
VCT SW PMTCT
Unit cost
decrease
associated
with doubling
of scale 1
p-value of
regression
Coefficient of
determination
(R2)
Unit cost
decrease
associated with
doubling of
scale 1
p-value of
regression
Coefficient of
determination (R2)
Unit cost decrease
associated with
doubling of scale 1
p-value of
regression
Coefficient of
determination
(R2)
India 32.5% < 0.001 0.83 41.8% < 0.001 0.88 23.0% 0.038 0.42
Mexico 15.3% 0.066 0.20
Russia 27.2% 0.021 0.28 41.0% < 0.001 0.84
South
Africa
2.4% 0.033 0.33 31.2% 0.015 0.38
Uganda 27.5% 0.045 0.29
STI IEC RR
India 52.4% 0.050 0.42
Mexico 32.3% 0.038 0.70 46.3% < 0.001 0.91
Russia 58.0% 0.385 0.26 34.5%/47.9% 20.004/0.022 0.45/0.962
South
Africa
Uganda
1. "Doubling" is a 100% increase in output from the first quartile output value.
2. Needle exchange programs/Rehabilitation-focused programs
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costs as shown in the data point in the lower right corner
of Figure 2. If this atypical program is removed, the R2 for
the remaining Indian sites is 0.72.
Sexually Transmitted Infection programs
For sexually transmitted infection (STI) programs, data
from three countries show at least modest scale effects
over a smaller range in scale (See Figure 3). Scale varies 50-
fold within Mexico, and 5-fold within Russia and India.
Efficiency (cost per first visit for suspected STI) varies 100-
fold within Mexico and 10-fold within Russia and India.
Over all three countries, this cost varies from $650 to
$0.82 per first visit. The proportion of variation explained
by scale also varies widely: 70% (Mexico), 26% (Russia),
and 42% (India). For Mexico, each doubling in scale is
associated with a 35% – 90% decrease in cost per first
visit, depending on starting point. For Russia, there is a
58% decrease in cost per first visit for a doubling in scale.
STI programs in India were one of two intervention-coun-
try pairs that exhibited an upturn in unit costs. After
declining from $53.77 per first visit at a program with 324
first visits, to $6.31 per first visit at a program with 1,357
first visits, unit cost rose to $27.33 for a program with
2,664 first visits.
Information, Education and Communication programs
PANCEA data on information, education and communi-
cation (IEC) programs are restricted to Mexico. Scale var-
ies widely: 10,000-fold, due to some programs having
large electronic media components (See Figure 4). Effi-
ciency (cost per hour of media contact) varies 5,000-fold.
The proportion of variation explained by scale is 91%. A
doubling in scale results in a 64% drop in cost per hour of
contact and this association is highly statistically signifi-
cant (p-value < 0.001).
Risk reduction programs
Data on risk reduction program for injection drug users
come only from Russia. Most programs focus on needle-
syringe exchange. For these programs, scale varies 50-fold,
and efficiency varies 40-fold. Scale explains 45% of varia-
tion in efficiency (cost per 50 needles exchanged). A dou-
bling in scale is associated with a 34.5% reduction in cost
per output (p-value = 0.004).
Figure 3
$0
$1
$10
$100
$1,000
0 500 1,000 1,500 2,000 2,500 3, 000
1st Visits for STI diagnosis/ treatment ( scale)
Economic US$ per 1st visi
t
(unit cost)
Mexico
Russia
Indi a
Figure 1
$1
$10
$100
$1,000
1 10 100 1,000 10,000 100,000
Annual clients completing VCT (scale)
Cost per VCT completed (unitc ost)
Mex ic o
Uganda
Russia
Indi a
S. Africa
Figure 2
$0.01
$0.10
$1.00
$10.00
$100.00
$1,000.00
100 1,000 10, 000 100, 000 1,000, 000
Hours of contact (scale)
Cost per hour of contact
(unit cost)
Ind i a
Russia
S. Africa
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For inpatient rehabilitation programs (n = 4), scale
explains 96% of variation in efficiency (cost per treatment
episode). A doubling in scale is associated with a 47.9%
reduction in cost per output (p-value = 0.022). The two
programs that specialize in counseling also exhibit signif-
icant economies of scale, though this finding is weakly
suggestive only because of the sample size (See Figure 5).
Prevention of Mother to Child Transmission programs
For prevention of mother-to-child HIV transmission pro-
grams (PMTCT), we report data only from India (other
countries collected data from only one site each). For
these programs, scale varies 6-fold, and efficiency varies 2-
fold. Scale explains 42% of variation in efficiency (cost per
mother completing post-test counseling). A doubling in
scale from the first quartile output level is associated with
a 23% reduction in cost per output. As shown in Figure 6,
unit costs rise at output levels exceeding 10,000 women
per year who receive post-test counseling. However, this
up-turn in unit costs is due to only one data point. With-
out it, costs level off but do not increase. If the number of
mother-neonate pairs receiving nevirapine is used as the
output measure, we see strongly declining costs with scale
up to 264 per year and no indication of an up-turn in unit
costs (R2 = 0.84) (Figure not shown).
Discussion
With two exceptions, we found increasing efficiency
across the full range of scale examined, in each of the 16
country-intervention pairs. Although the shape and
strength of the association varied by country and interven-
tion type, we found cost per key output and scale to be
strongly and negatively correlated in almost all instances
– with the fraction of variation in efficiency explained by
scale ranging from 19% – 96%. Doubling in scale resulted
in reductions in unit costs averaging 34.2% across all
country-intervention pairs and ranging from 2.4% to
56%.
Over the scale that we examined, we saw no up-turn in
cost per key output except for STI and PMTCT programs in
India. The latter was due to only one data point (Figure 6).
This suggests that for programs in similar demographic
and epidemic settings one would expect to observe
Figure 6
$0.00
$0.50
$1.00
$1.50
$2.00
$2.50
$3.00
$3.50
$4.00
$4.50
$5.00
0 5,000 10,000 15,000 20,000
Mother s completing post-test counseling (scale )
Economic cost in US$ per mother completing post-test
counseling (unit cost)
Figure 4
$0.00
$0.01
$0.10
$1.00
$10.00
$100.00
1,000 10,000 100,000 1,000,000 10,000,000 100,000,000
Person-ho urs of e xposure (scale)
Economic US$ per person-hour of
exposure (unit cost)
Figure 5
$1
$10
$100
$1,000
$10,000
- 1,000 2,000 3,000 4,000 5,000
Clients se rve d per year (scale)
Economic cost in US$ per client served
(unit cost)
Rehab
Needle exchange
Counseling
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increasing efficiency at least up to similar levels of output.
This is an encouraging finding as it suggests that the cur-
rent global HIV prevention program will become less
costly over a wide range of expansion. However, this find-
ing must be tempered by the finding of an up-turn in unit
cost reported in the previous study of SW programs in
India [20], and by the up-turn reported here in PMTCT
and STI programs. It must also be tempered by unpub-
lished data from India by co-author Dandona using PAN-
CEA methods for a more recent fiscal year that suggest an
up-turn for VCT and SW programs. However, it is impor-
tant to note that with simple linear functions, the regres-
sion trends were downward sloping in all cases.
There are a number of possible and non-mutually exclu-
sive causes of the systematic economies of scale observed
in the PANCEA data. One is that busier facilities are able
to distribute their fixed costs over more cases, thus lower-
ing their unit costs. A second is that busier facilities are
better positioned to take advantage of potential lower
prices through bulk purchases, sharing of services and
other advantages of scale that lead to greater efficiency. Yet
a third mechanism by which large scale may reduce unit
costs is that program administrators learn how to inte-
grate HIV prevention activities with routine services,
reduce personnel "down-time," reduce supply loss and
breakage, and generally deploy their resources more effi-
ciently. This learning occurs over time and is thus an
"economy of time" rather than of scale per se. However, in
an era of program expansion, time and scale are highly
correlated.
The first explanation, i.e., that unit costs decline as fixed
costs are distributed over more output, is an arithmetic
and programmatic truism, and therefore must contribute
to the observed scale effects. The extent of that contribu-
tion depends on the portion that fixed costs constitute of
total costs. The larger the fixed cost portion, the stronger
the scale effect will be. Thus, it may be possible to increase
efficiency by either increasing demand for services or find-
ing ways to reduce costs that have been treated as fixed.
Because inputs were market priced, we introduced a bias
toward not being able to detect economies of scale arising
from the ability to make bulk purchases. The third expla-
nation, namely that program managers learn how to opti-
mize deployment of the resources over time, and thus
with scale, is supported by longitudinal analyses we con-
ducted in PANCEA and other analyses we are conducting
[11] (and unpublished data).
The purpose of the current analysis was to document the
relationship between scale and unit cost and not to for-
mally assess the relative contribution of each of these
three causes. Our team is planning further analyses of the
underlying determinants of efficiency using multivariate
regression techniques to shed light on this question. We
are currently analyzing longitudinal data on about 20% of
the PANCEA sites and applying multivariate analyses to
the existing data set in an effort to address these questions
more definitively.
Our analysis has a number of important limitations. The
data presented here are cross-sectional. Rather than docu-
menting the change in unit costs over time within expand-
ing programs, we tabulated the unit costs of many
programs simultaneously and measured the association
between scale and unit costs among them. Although these
analyses are strongly suggestive of economies of scale
within programs, they do not demonstrate it directly. In
addition, this study does not use multivariate statistical
methods to show the relative contribution of other possi-
ble predictors of unit costs, or to control for possible con-
founding factors. In particular, this paper does not assess
the possible role of service quality as a mediator between
unit cost and service volume. The effects of quality on unit
costs are unclear. Higher quality services may require
more resources and thus raise costs. It may also increase a
programs reputation, thereby lowering outreach and pro-
motion costs. By attracting more clients, it may also con-
tribute to lowering unit costs via other economy of scale
effects. We did not observe service delivery directly, and
meaningful measures of quality vary by intervention type.
However, for VCT programs, the largest program sub-set,
we regressed cost per client on numerous indicators of
program quality. We found no statistically significant
associations between indicators of service quality and unit
costs. For these reasons, it is conceivable, if unlikely, that
the differences observed reflect program differences other
than scale, but reflected in scale.
Because we did not observe an upturn in unit costs with
larger scale for the vast majority of the programs, we are
unable to address the question of what the optimal size of
different types of prevention modalities might be. That is,
we cannot identify optimal facility size, i.e., when it is
more efficient to open a new facility in a nearby commu-
nity rather than continue to expand services in an existing
one. We believe that the lesson for now is, more services
will usually reduce cost per service. Micro-economic the-
ory suggests that an upturn in unit costs may be a signal to
policy makers that the number of HIV prevention service
providers should be increased in the relevant setting [13].
Conclusion
The economies of scale data reported here may contribute
to the ongoing effort to project the future cost of the glo-
bal HIV prevention effort as it scales up. To date these
efforts have been hampered by the inability to adjust costs
by scale. For the five interventions for which we have a
sufficient amount of data, the present findings may be
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Page 9 of 9
(page number not for citation purposes)
able to provide useful guidance. As more PANCEA-type
data are collected by us and by other research teams, it
should be possible to add more points to this data set and
thus arrive at increasingly precise estimates of the strength
and range over which scale effects operate.
Competing interests
The author(s) declare that they have no competing inter-
ests.
Authors' contributions
All named authors were responsible for developing and
integrating major elements of PANCEA methods, adapt-
ing those methods for application in their respective
countries, and several contributed data for this paper
Elliot Marseille lead the process of data analysis and inter-
pretation, and the manuscript drafting and revision effort,
with significant contributions from James G. Kahn, Lalit
Dandona, Nell Marshall, Paul Gaist, Brandi Rollins, Mat-
tias Lundberg, Sergio Bautista, Jo-Ann Du Plessis, and
Mead Over. All authors read and approved the final man-
uscript.
Acknowledgements
Our thanks to Michelle Boontanom, Emily Felt, Adam Nguyen, Raul Rey-
noso, Ashby Wolfe, and Rudy Rucker at the Graduate School of Public Pol-
icy, University of California, Berkeley, for their capable assistance in the
analysis of the relationship between VCT service quality and cost. This
research was supported by the NIH Task Order #7 contract 282-98-0026
and by the National Institute on Drug Abuse through grant R01 DA15612.
The findings and conclusions in this report are those of the authors and do
not necessarily represent the views of the National Institutes of Health.
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