Using Interrupted Time Series Analysis to Measure the Impact of Legalized Syringe Exchange on HIV Diagnoses in Baltimore and Philadelphia

Abstract

Background:
Syringe exchange programs (SEP) reduce HIV incidence associated with injection drug use (IDU), but legislation often prohibits implementation. We examined the policy change impact allowing for SEP implementation on HIV diagnoses among people who inject drugs in 2 US cities.

Setting:
Philadelphia, PA, and Baltimore, MD.

Methods:
Using surveillance data from Philadelphia (1984-2015) and Baltimore (1985-2013) for IDU-associated HIV diagnoses, we used autoregressive integrated moving averages modeling to conduct 2 tests to measure policy change impact. We forecast the number of expected HIV diagnoses per city had policy not changed in the 10 years after implementation and compared it with the number of observed diagnoses postpolicy change, obtaining an estimate for averted HIV diagnoses. We then used interrupted time series analysis to assess the immediate step and trajectory impact of policy change implementation on IDU-attributable HIV diagnoses.

Results:
The Philadelphia (1993-2002) model predicted 15,248 new IDU-associated HIV diagnoses versus 4656 observed diagnoses, yielding 10,592 averted HIV diagnoses over 10 years. The Baltimore model (1995-2004) predicted 7263 IDU-associated HIV diagnoses versus 5372 observed diagnoses, yielding 1891 averted HIV diagnoses over 10 years. Considering program expenses and conservative estimates of public sector savings, the 1-year return on investment in SEPs remains high: 243.4M(Philadelphia)and243.4 M (Philadelphia) and 62.4 M (Baltimore).

Conclusions:
Policy change is an effective structural intervention with substantial public health and societal benefits, including reduced HIV diagnoses among people who inject drugs and significant cost savings to publicly funded HIV care.

Full text

SUPPLEMENT ARTICLE
Using Interrupted Time Series Analysis to Measure the
Impact of Legalized Syringe Exchange on HIV Diagnoses in
Baltimore and Philadelphia
Monica S. Ruiz, PhD, MPH,
a
Allison O’Rourke, MPH,
b
Sean T. Allen, DrPH, MPH,
c
David R. Holtgrave, PhD,
c
David Metzger, PhD,
d
,
e
Jose Benitez, MSW,
f
Kathleen A. Brady, MD,
g
C. Patrick Chaulk, MD, MPH,
h
and Leana S. Wen, MD
i
Background: Syringe exchange programs (SEP) reduce HIV
incidence associated with injection drug use (IDU), but legislation
often prohibits implementation. We examined the policy change
impact allowing for SEP implementation on HIV diagnoses among
people who inject drugs in 2 US cities.
Setting: Philadelphia, PA, and Baltimore, MD.
Methods: Using surveillance data from Philadelphia (1984–2015)
and Baltimore (1985–2013) for IDU-associated HIV diagnoses, we
used autoregressive integrated moving averages modeling to conduct
2 tests to measure policy change impact. We forecast the number of
expected HIV diagnoses per city had policy not changed in the 10
years after implementation and compared it with the number of
observed diagnoses postpolicy change, obtaining an estimate for
averted HIV diagnoses. We then used interrupted time series analysis
to assess the immediate step and trajectory impact of policy change
implementation on IDU-attributable HIV diagnoses.
Results: The Philadelphia (1993–2002) model predicted 15,248
new IDU-associated HIV diagnoses versus 4656 observed diagno-
ses, yielding 10,592 averted HIV diagnoses over 10 years. The
Baltimore model (1995–2004) predicted 7263 IDU-associated HIV
diagnoses versus 5372 observed diagnoses, yielding 1891 averted
HIV diagnoses over 10 years. Considering program expenses and
conservative estimates of public sector savings, the 1-year return on
investment in SEPs remains high: $243.4 M (Philadelphia) and
$62.4 M (Baltimore).
Conclusions: Policy change is an effective structural intervention
with substantial public health and societal benefits, including
reduced HIV diagnoses among people who inject drugs and
significant cost savings to publicly funded HIV care.
Key Words: injection drug use, HIV, syringe exchange, policy
change, cost-effectiveness
(J Acquir Immune Defic Syndr 2019;82:S148–S154)
INTRODUCTION
Syringe exchange programs (SEP), also known as needle
exchange programs or syringe access programs, are an essential
component to preventing HIV/AIDS among people who inject
drugs (PWID). Studies have shown that SEP utilization is
associated with reductions in bloodborne infections among
PWID; research from Tacoma, WA,
1
New Haven, CT,
2
and
New York City, NY,
3
has demonstrated that SEP implementa-
tion was associated with significant reductions in hepatitis B and
C incidence (80% reduction)
1
and HIV incidence (estimated
33%reductioninNewHavenand70%reductioninNew
York).
2,3
Despite evidence demonstrating the public health
utility of SEPs,
4–9
federal and state policies (eg, drug parapher-
nalia laws) may limit their implementation. Other policies more
explicitly affect SEP operations. In 1988, Congress passed
legislation prohibiting the use of federal monies to support SEPs.
Aside from a brief period (2009–2012) during which the
restriction was removed, the legislation remained until Decem-
ber2015when,largelyinresponsetoHIVoutbreaksamong
suburban and rural populations injecting prescription opioids,
the language was modified to allow federal monies to support
SEP operations except for purchasing injection equipment.
10
From the
a
Department of Prevention and Community Health, Milken Institute
School of Public Health, George Washington University, Washington, DC;
b
Department of Sociology, Center for Health, Risk, and Society, American
University, Washington, DC;
c
Department of Health, Behavior, and
Society, Johns Hopkins Bloomberg School of Public Health, Baltimore,
MD;
d
Department of Psychiatry, Perelman School of Medicine, University
of Pennsylvania, Philadelphia, PA;
e
Treatment Research Institute, Phila-
delphia, PA;
f
Prevention Point Philadelphia, Philadelphia, PA;
g
AIDS
Activities Coordinating Office, Philadelphia Department of Public Health,
Philadelphia, PA;
h
Bureau of HIV/STD Services, Baltimore City Health
Department, Baltimore, MD; and
i
Office of the Commissioner, Baltimore
City Health Department, Baltimore, MD.
This publication resulted in part from research supported by the Penn Center
for AIDS Research (CFAR) (P30 AI 045008 - Ronald Collman, PI), the
Penn Mental Health AIDS Research Center (PMHARC) (P30 MH
097488 - Dwight Evans, PI) and the CFAR Social & Behavioral Science
Research Network National Scientific Meeting (SBSRN) (R13 HD
074468 - Michael Blank, PI).
The authors have no conflicts of interest to disclose.
Correspondence to: Monica S. Ruiz, PhD, MPH, Milken Institute School of
Public Health, George Washington University, 950 New Hampshire
Avenue, Suite 300, Washington, DC 20052 (e-mail: msruiz@gwu.edu).
Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc.
This is an open-access article distributed under the terms of the Creative
Commons Attribution-Non Commercial-No Derivatives License 4.0
(CCBY-NC-ND), where it is permissible to download and share the
work provided it is properly cited. The work cannot be changed in any
way or used commercially without permission from the journal.
S148 |www.jaids.com J Acquir Immune Defic Syndr Volume 82, Supplement 2, December 1, 2019

SEP Case Histories: Philadelphia
and Baltimore
For Philadelphia and Baltimore in the 1990s, changing
paraphernalia legislation was critical to creating SEPs.
11
SEPs in Philadelphia originated when activists orga-
nized a community response to rising HIV infection rates
among PWID.
11
Pennsylvania’s paraphernalia laws were
conflicting: syringe possession and distribution were illegal
in Philadelphia,
12
but the Disease Prevention and Control Act
of 1955 authorized syringe distribution as a disease pre-
vention activity within individual cities. Seeing the AIDS
epidemic as a public health emergency, activists felt that the
Disease Prevention and Control Act authorized SEPs as
a public health measure. In late 1991, Prevention Point
Philadelphia (PPP) was created, operating illegally while
working with and gaining support from the health commis-
sioner, officials at the Department of Health’s AIDS Office
(now the AIDS Activities Coordinating Office), and the
mayor.
13
Despite state disapproval, the mayor signed Exec-
utive Order 4–92 on July 27, 1992, declaring HIV/AIDS
a public health emergency in Philadelphia and authorizing
SEPs as a measure to address it.
14
On August 1, 1992, PPP
held its first day of legal syringe exchange. Although data
pertaining to syringe distribution from the initial years of PPP
are not available, SEP operations’data from 1998 indicate
that over 400,000 syringes were distributed during this year,
with that amount almost doubling to 810,000 in 1999. In each
of these years, over 2000 new clients were registered. PPP
remains the sole officially recognized SEP in Philadelphia,
providing syringe access and harm reduction services
(including medical care, wound care, HIV/HCV testing,
overdose prevention and reversal training, linkage to drug
treatment, and medication-assisted treatment) through munic-
ipal and private support.
15
Baltimore’s legislative environment was also the big-
gest obstacle to SEP implementation. Maryland’s Uniform
Controlled Dangerous Substances Act of 1970 made drug
paraphernalia possession illegal. For SEPs to open in
Baltimore, state laws needed to change. The impetus for
change came in 1992 from the city’s own leadership: the
mayor and the health commissioner. They lobbied the state
legislature for exemptions to existing paraphernalia laws for
Baltimore City so the Health Department could legally
distribute sterile injection equipment. Early attempts at policy
change were met with resistance from the Governor and other
state legislators, but eventually, Senate Bill 402 was signed in
to law on April 2, 1994,
16
and went into effect on June 1,
1994. Since then, the Baltimore City Health Department has
run the SEP.
17
In addition to syringe access services, the SEP
provides harm reduction education, linkage to addiction
treatment services (including maintaining same day treatment
openings), testing and counseling for HIV and syphilis, and
opioid overdose response training.
17
Why Policy Change Matters
These stories show 2 different mechanisms of policy
change for SEP establishment; they also underscore the
importance of policy change as a structural intervention for
HIV prevention among PWID in that it changes individuals’
risk environment without changing their behaviors or social
interactions.
18
Our study in the District of Columbia (DC)
found that changing legislation to allow the use of municipal
revenue for SEPs in 2008 was associated with an estimated
120 averted HIV diagnoses in PWID in the first 2 years after
policy change and an estimated savings of $44.3 million in
health care costs associated with HIV treatment.
19
Given the historical contexts of Philadelphia and
Baltimore, would one expect the same magnitude of epidemic
impact that was observed in DC? We examined this question
using similar methodologies used in the examination of
policy change impact in DC.
19
Although previous research
has examined the association between presence of SEPs and
HIV incidence, these studies were more ecological in nature.
We build on these previous analyses by not only examining
how SEP implementation affected the HIV epidemic in each
city but also by attempting to isolate the epidemic impact of
policy change specifically on the numbers of new
HIV diagnoses.
METHODS
Using autoregressive integrated moving averages (ARI-
MA) modeling, forecasts were created to estimate the
expected annual number of new HIV diagnoses that would
have occurred in the 10 years had policies not changed.
Forecasted numbers for each city were then compared with
observed numbers of diagnoses to calculate the averted new
HIV diagnoses that could be attributed to legal SEP
implementation. We then used interrupted time series analysis
(ITSA) to assess the impact that policy change (the “inter-
ruption”) had on injection drug use (IDU)–associated HIV
diagnoses, both immediately and as a trend change. ITSA
examines temporally ordered data to determine whether an
experimental manipulation or intervention produced a reliable
change in data
20,21
while also allowing models to account for
baseline levels and trends present in the data. Henceforth, we
refer to observations before the interruption as the “pre-
implementation period”and those after the interruption as the
“postimplementation period.”
The outcome measure for both cities was IDU-
associated HIV diagnoses. In Philadelphia, data were ex-
tracted from Philadelphia’s Enhanced HIV/AIDS Reporting
System (eHARS), a population-based registry containing
information on all HIV/AIDS diagnoses reported to the
Philadelphia Department of Public Health AIDS Activities
Coordinating Office Surveillance Unit since 1984. eHARS
contains information abstracted from medical records, includ-
ing HIV transmission risk (eg, IDU and MSM/IDU) and
laboratory reporting of all CD4 cell counts and HIV-1 RNA
levels. Thus, eHARS contains data from all people living with
HIV (PLWH) diagnosed in Philadelphia.
Annual numbers of HIV diagnoses in Baltimore were
extracted from the Baltimore City Annual HIV Epidemiolog-
ical Profile,
22
which contains data reported to the state
through December 31, 2014. Annual percentages of diagno-
ses attributed to each exposure category are reported for the
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Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. www.jaids.com |S149

years 1985–2013. To determine the annual number of IDU-
associated HIV cases, the percentages of new HIV diagnoses
with a known exposure risk were multiplied by each IDU-
associated exposure category (IDU and MSM/IDU). These 2
numbers were combined for each year to represent the total
number of new IDU-associated HIV diagnoses.
ARIMA models were fit to the preimplementation data
for each city using Box and Jenkins
23
methods. Outlier
detection was completed to identify significant (a,0.01)
shift and additive observations. Shift outliers were addressed
in the model by entering dichotomous variables assigning 0 to
observations prior to and 1 to all other observations including
and following the identified shift outlier. For additive outliers,
dichotomous variables were added to the model, assigning 1
to the additive outlier and 0 to all other observations.
ITSA models evaluated 2 types of impact—step change
and slope change—on new HIV diagnoses. Step change tests
for an immediate significant change between HIV diagnoses
in the last preperiod and first postperiod observations. It was
measured with a dichotomous variable that assigned 0 to all
prepolicy and 1 to all postimplementation observations. Slope
change tests for significant changes in trend and direction of
the number of HIV diagnoses across the preperiod versus the
postperiod. It was measured using a continuous variable that
assigned 0 to all preimplementation observations and 1 to the
first postinterruption observation, with subsequent observa-
tion values increasing by 1 (1, 2, ., n).
For the ITSA, the year ending after the date of policy
change was used as the interruption. In the 6 months after each
policy change, surveillance efforts and normal testing mecha-
nisms would have been more likely to detect those who are
already HIV-positive. Therefore, the real policy impact would
have occurred after persons already living with HIV and
previously undiagnosed were detected and after legal SEPs were
operating and effectively reaching PWID. In Philadelphia, legal
SEP began on August 1, 1992, so the data were divided into 2
periods: preimplementation (1984 through 1992) and postim-
plementation (1993 through 2015). Baltimore’s legal SEP began
on June 1, 1994, so data were similarly divided into preimple-
mentation (1985 through 1994) and postimplementation (1995
through 2013) periods. In using annual data, our analyses attempt
to conservatively account for the possible lag time between when
the policy changed and when it started to have epidemic impact.
In addition, we replicated these methods in Baltimore
using HIV diagnoses data among MSM without IDU
exposure to examine the true impact of SEP utilization on
new HIV diagnoses (ie, as a control). Notably, these analyses
were only possible in Baltimore given HIV case-reporting
data availability. Given that MSM in Baltimore would be
similar to the PWID population in terms of exposure to public
health efforts (eg, changes in HIV testing and access to HIV
treatment) that would have been available during the same
time period, these analyses allow us to control for other
variables that may have affected new cases of HIV and better
understand the true impact of SEP implementation on IDU-
associated HIV diagnoses. All analyses were completed using
SAS v9.4. This research was determined by the George
Washington University’s Institutional Review Board as
exempt from oversight (IRB #051106).
RESULTS
Descriptive measures for the epidemiologic data are
presented in Table 1. For Philadelphia, we observed a non-
significant preimplementation to post implementation
decrease in the mean number of new IDU-associated HIV
diagnoses (419.9 and 291.6, respectively) as well as a signif-
icant preimplementation to post implementation decrease in
the MSM/IDU exposure category (77.7 and 35.6, P,0.05).
For Baltimore, we observed a significant preimplementation
to postimplementation decreases in both in the mean number
of IDU-associated HIV diagnoses (607.9 and 357.0, P,
0.05) and the mean numbers of new diagnoses attributed to
each IDU exposure category (IDU only: 542.8 and 332.2, P
,0.05, MSM/IDU: 65.0 and 24.8, P,0.05).
Philadelphia
For Philadelphia, an ARIMA (0, 1, 0) model with 4
outliers—shift outliers at 1989 and 1990 and additive outliers
at 2002 and 2005—was determined to be the best fit for the
data. The 1989 and 1990 shift outliers were included in all
analyses; the 2002 and 2005 additive outliers were only
included in the ITSA. The model forecasts 15,248 new IDU-
associated HIV diagnoses in Philadelphia between 1993 and
2002, whereas 4656 IDU-associated HIV diagnoses were
reported (Fig. 1). This is a difference of 10,592 averted
diagnoses of HIV over 10 years (approximately 1059 averted
diagnoses annually). Using annual HIV case data from 1984
to 2015, ITSA was completed to determine whether SEP
implementation resulted in an immediate step or trend change
between the preimplementation and postimplementation peri-
ods. A significant negative step change (B = 2162.5,
P,0.001) was identified, as was a significant decreasing
slope change (B = 2115.5, P,0.001) (Table 2).
Baltimore
For Baltimore, an ARIMA (1, 1, 0) model with one
order of nonseasonal differencing and a single autoregressive
TABLE 1. Annual New Diagnoses of IDU-Associated HIV
Infection: Baltimore and Philadelphia
Prepolicy Period,
Mean (SD)
Postpolicy Period,
Mean (SD)
Overall,
Mean (SD)
Philadelphia
IDU exposure 342.2 (300.4) 256.0 (179.3) 280.25 (218.3)
MSM and
IDU
exposure
77.7 (39.6) 35.61 (17.7)* 47.4 (31.6)
Total 419.9 (336.9) 291.6 (196.3) 327.7 (245.1)
Baltimore
IDU exposure 542.8 (267.5) 332.2 (211.7)* 404.8 (249.4)
MSM and
IDU
exposure
65.0 (21.6) 24.78 (16.0)* 38.7 (26.3)
Total 607.9 (281.0) 357.0 (226.7)* 443.48 (270.4)
*Student’st-test, P,0.05 comparing pre interruption and postinterruption values.
Ruiz et al J Acquir Immune Defic Syndr Volume 82, Supplement 2, December 1, 2019
S150 |www.jaids.com Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc.

term was determined as the best fit for the data. The 10-year
forecast predicted 7263 new IDU-associated HIV diagnoses
in Baltimore between 1995 and 2004, whereas 5372 diagno-
ses were reported (Fig. 2). This is a difference of 1891 averted
HIV diagnoses over 10 years, with 207 averted cases in the
first 5 years (11% of total predicted averted cases) and 1684
in years 6 through 10 (89% of total predicted averted cases).
Using surveillance data for 1985 to 2013, ITSA was
completed to assess significant immediate and slope changes
between the preimplementation and postimplementation peri-
ods. No significant immediate step (B = 55.34 P= 0.4368) or
slope (B = 283.41, P= 0.0852) change was identified
(Table 2).
With respect to the control model in Baltimore that used
MSM-attributed HIV diagnoses, the ITSA found no signifi-
cant immediate step change or slope change for new cases of
HIV after SEP implementation (Fig. 3). In addition, the
forecasting showed only a small difference between the
observed (1,345) and expected (1,776) cases of MSM-
attributed HIV in the 10 years after the SEP policy change.
DISCUSSION
These analyses demonstrate that SEP implementation in
Philadelphia and Baltimore was associated with significant
overall reductions in IDU-associated HIV diagnoses. Phila-
delphia’sfindings are particularly striking: the significant step
and slope changes observed indicate that policy change and
SEP implementation occurred in the same year as the number
of HIV diagnoses was peaking. Although it cannot be stated
with 100% certainty that the epidemic trajectory would have
leveled after reaching that apex, the ARIMA forecast suggests
that diagnoses would have continued to rise had SEP
implementation not taken place. The significant level-shift
outliers identified in 1989 and 1990 during the prepolicy
change period indicate that Philadelphia had a significant
increase in new HIV diagnoses during these 2 consecutive
years that persisted for many years following. The additive
outliers identified in 2002 and 2005 mark 2 years that had
significantly higher numbers of reported new HIV cases;
however, these changes did not persist or impact observations
seen in the following years. Of interest and likely the
explanation for these 2 outliers, Philadelphia moved to
code-based reporting in 2002 and name-based reporting in
2005. These data support the possibility that the policy
change in Philadelphia may have capped the peak of IDU-
associated diagnoses in the city, explaining the rapid decrease
in IDU-associated diagnoses after 1995.
The Baltimore data also showed a decrease in IDU-
associated HIV diagnoses after the policy change.
Although the surveillance data show that IDU-associated
HIV diagnoses had begun to stabilize and decrease slightly
before SEP implementation, the addition of SEPs contrib-
uted to a more rapid decline. The comparison of the more
rapid decline in IDU-associated cases and the slower
decline in the number of MSM-associated cases indicates
that although other factors occurring within Baltimore
likely played some role in the decrease in HIV seen among
PWID, the vast majority of this decrease can be attributed
to SEP implementation.
FIGURE 1. Forecasted versus actual
diagnoses of IDU-associated HIV infec-
tion in Philadelphia during the 10 years
after the change in syringe exchange
policy.
TABLE 2. Interrupted Time Series Analysis: Philadelphia and
Baltimore
Coefficients
t
Value P
Philadelphia ARIMA (0, 1, 0)
Constant 79.0 6.57 ,0.0001
Shift outlier—1989 103.0 3.24 0.0035
Shift outlier—1990 149.0 4.68 ,0.0001
Additive outlier—2002 90.0 4.32 0.0002
Additive outlier—2005 117.53 3.9 0.0007
Immediate effect of policy
implementation
2162.5 25.39 ,0.001
Change in trend postpolicy
implementation
2115.5 28.47 ,0.001
Baltimore ARIMA (1,1,0)
Constant 51.30 1.38 0.1790
AR term 0.41 1.99 0.0576
Immediate effect of policy
implementation
55.33 0.79 0.4368
Change in trend postpolicy
implementation
283.41 21.80 0.0852
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Limitations must be acknowledged in this research, the
first pertaining to the quantity and quality of available
surveillance data for each city. Although AIDS case surveil-
lance in both cities began in the mid-1980s, HIV case
reporting (either by name or code) was implemented much
later; in Baltimore, code reporting began in 1994 and name
reporting began in 2007, whereas in Philadelphia, code
reporting began in 2002 and name reporting began in 2005.
Since SEPs in both cities started in the early to mid-1990s,
there were fewer observations of annual HIV diagnoses
before the policy change event despite having sufficient
numbers of observations to meet the minimum requirements
for ARIMA modeling. More prepolicy change observations
would have facilitated a more “finely tuned”model that better
reflects longitudinal trends in IDU-associated HIV diagnoses.
Also, our analyses modeled HIV diagnoses rather than
incidence due to data limitations. Whether diagnoses are
a good proxy for incidence depends on various factors,
including stage of the local HIV epidemic, the number of
PLWH unaware of their status, and the availability of local
testing programs. Furthermore, data obtained from city health
departments for these analyses may reflect issues present in
disease surveillance systems, including inconsistencies in
HIV/AIDS case reporting and variability in PWIDs’HIV
testing patterns. Effects observed in both cities may actually
underestimate impact given potential overlaps between diag-
noses of IDU-associated and heterosexual transmission, as
well as the availability and utilization of other interventions
for PWID such as addiction treatment and access to
antiretroviral medication.
Afinal point of consideration pertains to the determi-
nation of HIV epidemic impact of policy change within the
PWID population when there are no precise size estimates of
this population. Although estimates of the overall PWID
population has remained stable from 1992 to 2002, there have
been estimates of growing PWID populations in Baltimore
(168–336 per 10,000 population) and the Philadelphia–New
Jersey MSAs (from 151 to 173 per 10,000 population).
24
Given that the time period for these estimates is similar to that
of our analyses, we are confident that our estimates are
measuring true population impact.
Our findings also demonstrate that averted HIV diag-
noses translated to cost savings for cities where most PLWH
are recipients of publicly funded healthcare. The forecasts
estimated an average of 1059 HIV diagnoses in Philadelphia
and 189 HIV diagnoses in Baltimore averted annually.
Multiplying the lifetime costs of HIV treatment per person
($229,800)
25
by the average number of diagnoses averted
annually in both cities yields an estimated annual saving of
$243.4 million for Philadelphia and $62.4 million for
Baltimore. Considering diagnoses averted over the 10-year
modeled period, the lifetime cost savings associated with
averted HIV diagnoses stemming from policy change to
support SEPs may be more than $2.4 billion and $624 million
FIGURE 2. Forecasted versus actual
diagnoses of IDU-associated HIV diag-
noses in Baltimore during the 10 years
after the change in syringe exchange
policy.
FIGURE 3. Forecasted versus actual
diagnoses of IDU-associated and MSM-
associated HIV diagnoses (control case
scenario) in Baltimore during the 10
years after the change in syringe
exchange policy.
Ruiz et al J Acquir Immune Defic Syndr Volume 82, Supplement 2, December 1, 2019
S152 |www.jaids.com Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc.

dollars for Philadelphia and Baltimore, respectively. Because
SEPs are relatively inexpensive to operate,
26
overall cost
savings are substantial even when deducting program oper-
ational costs from the total amount. Considering annual
program expense ($390,000 in 2011 for Philadelphia
27
and
$800,000 estimated in FY 2017 for Baltimore
28
) (Kathleen
Goodwin, Baltimore City Health Department, personal
communication, January 3, 2017) and cost savings in each
city, and a conservative estimate that 75% of these savings
would be experienced in the public sector, the 1-year return
on investment in SEPs remains in the hundreds of millions
of dollars ($182.5 M for Philadelphia, $46.8 M for
Baltimore). Small investments in SEPs may yield large
savings in HIV treatment costs, so implementing SEPs may
liberate resources for other important interventions, such as
expanded access to medication-assisted treatment, overdose
prevention, and housing.
Another implication pertains to how variations in SEP
implementation may have influenced intervention effective-
ness. Policies governing SEPs affect not only the overall
number of syringes distributed annually but also the ability of
PWID to obtain sufficient coverage for all injection events.
For example, PPP’s clients may exchange syringes for
themselves and others; recent data show that the mean
number of syringes exchanged per exchange event increased
from 1.53 in 1999 to 1.82 in 2014.
13
In addition, PPP’s
annual syringe distribution has consistently increased from
approximately 811,000 in 1999 to 1.2 million in 2014,
13
allowing for greater coverage of injection events and more
opportunities for disease prevention.
By contrast, Baltimore’s SEP had a one-for-one (1:1)
exchange policy from 1994 to 1999 but, in 2000, switched to
a more restrictive policy, where clients were allowed 1:1
exchange for program-distributed syringes but could receive 1
sterile syringe in exchange for 2 nonprogram syringes. From
2005 to 2014, the SEP returned to the less restrictive 1:1
policy, after which they shifted to a need-based distribution
model whereby PWID could access as many syringes as
needed. Baltimore City’s health commissioner estimated that
moving from the 1:1 to the needs-based distribution policy
could increase coverage of injection events from 42% to
61%.
29
More flexible approaches to syringe access in
Baltimore could have resulted in greater injection coverage
and more dramatic declines in IDU-associated HIV diagnoses
earlier. Regulations limiting clean needle and syringe distri-
bution are important operational issues to consider if policy
changes supporting harm reduction for PWID are to have
optimal impact.
This research provides additional support for the
effectiveness of policy change as a structural intervention
for HIV prevention among PWID and the utility of syringe
access as an effective, evidence-based approach to promote
PWID health. The need for such approaches is particularly
relevant given the current state of the opioid epidemic in the
United States and the resurgence of IDU-associated HIV
outbreaks in suburban and rural areas. A critical lesson
learned from the Indiana HIV outbreak was that had the
SEP been implemented earlier in the course of that outbreak,
many infections could have been averted. Communities
throughout the United States which are vulnerable to IDU-
associated HIV outbreaks should consider the potential public
health benefits, such as those experienced in Philadelphia and
Baltimore; they may gain from implementing SEPs as they
are one of our most powerful HIV prevention strategies for
PWID populations.
ACKNOWLEDGMENTS
This work is part of a larger project—DC POINTE:
Policy Impact on the Epidemic—whose main objective is to
examine the epidemic impact of policy change as a structural
intervention for HIV prevention for PWID in the District of
Columbia. This research was supported by a grant from the
National Institute on Drug Abuse (NIDA) to M.S.R.
(R01DA031649). The authors of this paper have no conflicts
of interest to declare.
We would also like to acknowledge the infrastructure,
core, and administrative support provided by the District of
Columbia Center for AIDS Research (CFAR; P30AI087714),
the Penn Center for AIDS Research (CFAR; P30 AI045008),
the Penn Mental Health AIDS Research Center (PMHARC;
P30MH097488), and the Johns Hopkins University Center
for AIDS Research (CFAR; P30AI094189). The CFARs are
NIH funded programs which are supported by the following
NIH Co-Funding and Participating Institutes and Centers:
NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC,
NIGMS, NIDDK, and OAR. The content of this paper is
solely the responsibility of the authors and does not
necessarily represent the official views of the NIH.
The authors would like to express gratitude to the
Baltimore Department of Health and Mental Hygiene, the
Philadelphia Department of Public Health, the Baltimore
City Syringe Exchange program, and Prevention Point
Philadelphia. We would also like to thank Danielle Fiore
for her assistance with the abstraction of the Philadelphia
surveillance data.
L.S.W. is currently a Visiting Professor of Health
Policy and Management and Distinguished Fellow in the
Fitzhugh Mullan Institute for Health Workforce Equity at the
Milken Institute School of Public Health, George Washington
university. D.R.H. is currently the Dean and SUNY Empire
Innovation Professor at the University of Albany, State
University of New York (SUNY). C.P.C. is currently retired
from the Baltimore City Health Department.
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S154 |www.jaids.com Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc.