Effect of Medicaid Expansions on HIV Diagnoses and
Pre-Exposure Prophylaxis Use
Bita Fayaz Farkhad, PhD,
David R. Holtgrave, PhD,
Dolores Albarracín, PhD
Introduction: Increased insurance coverage and access to health care can increase identiﬁcation of
undiagnosed HIV infection and use of HIV prevention services such as pre-exposure prophylaxis.
This study investigates whether the Medicaid expansions facilitated by the Affordable Care Act had
Methods: A difference-in-differences design was used to estimate the effects of the Medicaid
expansions using data on HIV diagnoses per 100,000 population, awareness of HIV status, and pre-
exposure prophylaxis use. The analyses involved ﬁrst calculating differences in new diagnoses and
pre-exposure prophylaxis use before and after the expansions and then comparing these differences
between treatment counties (i.e., all counties in states that expanded Medicaid) and control counties
(i.e., all counties in states that did not expand Medicaid). Further analyses to investigate mecha-
nisms addressed associations with HIV incidence, rates of sexually transmitted infections, and sub-
stance use. Analyses were conducted between August 2019 and July 2020.
Results: Medicaid expansions were associated with an increase in HIV diagnoses of 0.508 per
100,000 population, or 13.9% (p=0.037), particularly for infections contracted via injection drug use
and among low-income, rural counties with a high share of pre−Affordable Care Act uninsured
rates that were most likely to be affected by the expansions. In addition, Medicaid expansions were
associated with improvements in the knowledge of HIV status and pre-exposure prophylaxis use.
There was no impact of the expansions on incident HIV, substance use, or sexually transmitted
infection rates with the exception of gonorrhea, which decreased after the expansions. Altogether,
these results suggest that the changes in new HIV diagnoses, awareness of HIV status, and pre-
exposure prophylaxis were not simply because of a higher incidence or an increase in infection risk.
Conclusions: Medicaid expansions were associated with increases in the percentage of people liv-
ing with HIV who are aware of their status and pre-exposure prophylaxis use. Expanding public
health insurance may be an avenue for curbing the HIV epidemic.
Am J Prev Med 2021;60(3):335
342. © 2020 American Journal of Preventive Medicine. Published by Elsevier
Inc. All rights reserved.
The Affordable Care Act (ACA) expanded eligi-
bility for the Medicaid program as an unprece-
dented step to increase access to health care.
Through this act, a program that previously covered
only low-income children, pregnant women, adults with
disabilities, and very low-income parents was expanded
to include all adults in families with incomes <138% of
the federal poverty level. Although the initial ACA plans
were to apply to all states, a 2012 Supreme Court deci-
sion made the eligibility expansion optional and, as of
May 2020, only 35 states and the District of Columbia
have proceeded with the expansions. This paper consid-
ers the impact of the expansions on HIV diagnoses and
prevention, starting from the premise that HIV burdens
Department of Psychology, University of Illinois at Urbana-
Champaign, Champaign, Illinois; and
School of Public Health, University
at Albany, Albany, New York
Address correspondence to: Bita Fayaz Farkhad, PhD, University of
Illinois at Urbana-Champaign, College of Liberal Arts and Sciences, 603
East Daniel Street, Champaign IL 61820. E-mail: email@example.com.
© 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights
Am J Prev Med 2021;60(3):335−342 335
populations with signiﬁcant socioeconomic disadvan-
tages and living in underserved areas of the U.S. That is,
above and beyond possible changes in HIV incidence,
did the expansions increase diagnoses of HIV among
people who were previously unaware of their HIV sta-
tus? Did they increase access to pre-exposure prophy-
laxis (PrEP, a daily pill approved by the U.S. Food and
Drug Administration in 2012 and highly effective in pro-
tecting against HIV)
for eligible low-income individ-
uals through Medicaid? Although prior studies have
identiﬁed a positive impact of Medicaid expansions on
answers to these 2 speciﬁc research ques-
tions can offer important insights for implementing the
current national HIV prevention strategy and fulﬁll the
commitment to end the spread of HIV in the U.S. by
The beneﬁts of an early HIV diagnosis and PrEP are
broad and well known. An HIV diagnosis, currently
made an average of 3 years after infection,
is the precur-
sor of access to antiretroviral therapy, which also pre-
vents death and morbidity and the spread of the virus.
Furthermore, an HIV diagnosis reduces risk behav-
ior among people who were previously undiagnosed,
and the Centers for Disease Control and Prevention
(CDC) recommend frequent testing for populations
with high HIV risk and ≥1 test for all individuals at
some point in their lives.
PrEP also improves health
outcomes by preventing infection and is recommended
to men who have sex with men, people who inject drugs,
and heterosexually active adults.
though access to both HIV screening and PrEP is
facilitated by having health insurance, people living with
HIV and the populations that need PrEP are often poor
or underserved. These 2 aspects of HIV epidemiology
make the ACA a natural intervention to increase both
HIV diagnosis and PrEP uptake.
This study capitalizes on the variability across states
and years (Appendix Figure A1, available online) by
comparing changes in HIV-related outcomes in expan-
sion and nonexpansion states. Although the ACA pro-
moted health insurance coverage in numerous ways, this
study examines the expansions in the Medicaid program
and their impact on HIV diagnoses and PrEP use. For
states that have adopted Medicaid expansion, HIV
screening must be covered at no cost for the newly eligi-
ble populations under the ACA.
Likewise, PrEP is
paid for by state Medicaid programs.
Medicaid expansions should increase HIV diagnoses,
knowledge of HIV status, and PrEP use in a direct way.
In addition, they may also increase HIV diagnoses,
knowledge of HIV status, and PrEP use indirectly. First,
having health insurance may increase HIV diagnoses
and PrEP use by increasing contact with healthcare pro-
viders, who are advised to screen and provide counseling
for HIV to their patients.
Second, having health
insurance may increase coverage of substance use treat-
ment. Substance use treatment has been shown to facili-
tate HIV diagnoses
and may also increase awareness
of PrEP and thus PrEP use.
A model of the impact of the Medicaid expansions on
HIV diagnoses and PrEP use appears in Figure 1.As
shown, Medicaid expansions may improve access to care
Figure 1. A model of Medicaid expansions and outcomes.
Notes: The ﬁgure depicts a model of the impact of Medicaid expansions on HIV diagnoses and PrEP use. Medicaid expansions may improve access to
care and therefore increase HIV diagnoses, awareness of HIV status, and PrEP use. The effects of the expansions should be stronger for rural areas,
counties with higher levels of poverty, and counties with a higher share of pre-ACA rates of uninsured individuals. In the short run, no direct proximal
impact of the expansion on HIV incidence, other STIs, or substance use was predicted. ACA, Affordable Care Act; PrEP, pre-exposure prophylaxis; STI,
sexually transmitted infection.
336 Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
and therefore increase HIV diagnoses, awareness of HIV
status, and PrEP use. These increases should be stronger
for rural areas, counties with higher levels of poverty,
and counties with higher shares of pre-ACA rates of
uninsured individuals. The rationale for these predic-
tions is that the policy should have the most impact on
the most vulnerable areas. HIV incidence, rates of other
sexually transmitted infections (STIs), and substance use
also appear in Figure 1. However, these variables are not
connected directly to the expansions because they are
difﬁcult to specify a priori. First, better access and associ-
ated improvements in HIV screening and PrEP may
decrease HIV incidence over time. However, this effect
may be visible only over a long period. Second, the
effects of the expansions on STIs may be similar or dif-
ferent from those for HIV depending on the primary
route of HIV transmission. If the HIV diagnoses reﬂect
sexual transmission, then an increase in HIV diagnoses
may go hand in hand with an increase in STI rates. By
contrast, if the HIV diagnoses reﬂect drug injection
transmission, then an increase in HIV diagnoses may be
unrelated to the trend of STIs.
This study examines whether Medicaid expansions
affected HIV diagnoses and PrEP use. Although studies
have looked at the impact of the Medicaid expansions
on HIV testing,
this study is the ﬁrst to estimate the
impact of these expansions on diagnoses and PrEP use.
Further analyses also investigate whether any changes in
the diagnoses were because of infections via (1) hetero-
sexual contact, (2) injection drug use, (3) male-to-male
sexual contact, (4) male-to-male sexual contact and
injection drug use, and (5) other routes. To describe the
potential mechanisms, the effects of expansions on (1)
HIV incidence, (2) the rates of STIs, and (3) substance
use were also examined.
States’status regarding Medicaid expansion was obtained from
the Kaiser Family Foundation.
Appendix A, available online,
provides details on the categorization of states. To measure the
effect of these expansions on HIV-related outcomes, data from
several sources were used. Appendix Table F1, available online,
provides details on the deﬁnitions of each variable.
The HIV diagnoses per 100,000 population for each county-year
came from CDC’s National Center for HIV/AIDS, Viral Hepatitis,
STD (sexually transmitted disease) and TB (tuberculosis) Atlas
from 2010 to 2017.
The data on HIV diagnoses are also classiﬁed
into 5 transmission categories to which transmission may be
attributed: (1) heterosexual contact, (2) injection drug use, (3)
male-to-male sexual contact, (4) male-to-male sexual contact and
injection drug use, and (5) other.
One limitation of the CDC Atlas is that HIV data for counties
with <5 HIV cases or populations <100 are censored to ensure
conﬁdentiality of personally identiﬁable information. The cen-
sored cells were replaced with 0 in the main analyses, although
Appendix D, available online, tested the sensitivity of the results
to this choice by also imputing with 1, 2.5, and 4 and by using the
method proposed by Siegler and colleagues.
To supplement HIV diagnoses, the study involved analyses of
the estimated percentage of people living with HIV who were
diagnosed and thus know about their HIV infection. The state-
level estimates were obtained from CDC for the years 2010−2017.
To study the effects on PrEP use, the number of people who
had ≥1 day of prescribed PrEP in a year per 100,000 county resi-
dents were obtained from the AIDSVu for 2012−2017.
In the short run, the expansions were not expected to proxi-
mally and directly affect either HIV incidence or STIs (Figure 1).
However, possible effects were examined using (1) incidence esti-
mates per 100,000 population and (2) rates of other STIs obtained
from CDC for the years 2010−2017. Furthermore, supplementary
analyses examined the following possible expansion effects on
substance use and drug-related overdoses as an indication of
unsafe drug use: (1) the number of opioid prescriptions dispensed
per state by year from CDC and (2) the number of opioid-related
emergency room visits and inpatient stays per state by year from
the 2010−2016 Healthcare Cost and Utilization Program.
The models also included county-level measures of demo-
graphics, including the fraction of county population that is male
and aged 25−44 years, Black, and Hispanic and data from the
Bureau of Labor Statistics on unemployment rates. Finally, as
increases in HIV diagnoses may be because of injection drug use,
several drug policy indicator variables were constructed using
data from Meara et al.
to ensure that the estimates were not con-
founded by other simultaneous drug-related policy changes.
A difference-in-differences (DID) framework using longitudinal
data from treatment and control groups was employed to estimate
the effect of Medicaid expansions. First, differences in the out-
comes before and after the expansions were calculated, and then
those differences between treatment and control counties were
compared. The before period was 2010 through the year before
state expansion, and the after period was the expansion year
through 2017. The treatment counties are from 32 states plus the
District of Columbia, all of which expanded Medicaid to low-
income adults by December 2017, and the control counties are
from the 18 states that had not yet expanded Medicaid to this
population. The validity of this approach depends on parallel
trends assumption, namely that changes in HIV outcomes in the
nonexpansion counties provide a good counterfactual for the
changes that would have been observed in the treated counties in
the absence of the expansion.
The DID estimate is the coefﬁcient for the interaction term
between the post-expansion period indicator and the indicator
variable coded 1 for counties in states that opted to expand Medic-
aid eligibility by the end of 2017 and 0 otherwise. All models con-
trolled for county characteristics, state, and year indicators. State-
speciﬁc linear time trends were added to some of the speciﬁcations
Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
to additionally control for state-speciﬁc unobservables that vary
over time. In addition, results from models that included indicator
variables for treated counties before the Medicaid expansion and
1-year lag indicator were included. SEs were clustered at the state
The timing of the effects was investigated by constructing event
studies, in which a single expansion indicator variable was
replaced with a series of indicator variables representing the num-
ber of years relative to the expansion.
This analysis was used to
verify the validity of the parallel trends assumption. The analyses
were conducted between August 2019 and July 2020. Appendix B,
available online, provides additional details about the statistical
models. Appendix C, available online, tests for the validity of the
parallel trends assumption.
Summary statistics for the main variables used in the
analysis from 2010 to 2017 appear in Appendix
Table F2, available online. As shown, the mean for all
HIV cases was lower for the expansion states than non-
expansion states. At the same time, the use of PrEP was
greater in these areas.
Table 1 presents the DID estimates of how the expan-
sions affected HIV diagnoses. According to the regres-
sion result in Column 1, the Medicaid expansions were
associated with a statistically signiﬁcant increase in HIV
diagnoses, an average of 0.508 new cases per 100,000
population, which represents a 13.9% (0.508 £100/
3.659) increase from pre-expansion levels. Appendix
Table F3, available online, presents the analogous esti-
mates from a model that included state-by-year indica-
tors and estimates controlling for a 1-year leading
Figure 2A plots the DID estimates and their corre-
sponding 95% CIs, comparing HIV diagnoses in coun-
ties in expansion states with those in nonexpansion
states, relative to the year before treatment. Figure 2B
adds state-speciﬁc linear time trends. Points to the left of
the vertical line present the differences in treatment and
control counties before the expansion of Medicaid, sug-
gesting that the pre-expansion differences in new diag-
noses were not statistically signiﬁcant. At the time of the
Medicaid expansion, the difference in HIV diagnoses
between expansion and nonexpansion states increased
and remained above the earlier coefﬁcients in Years 2
and 3 (Figure 2A). Although the DID coefﬁcient esti-
mated in the year following the expansion was robust to
the inclusion of state-speciﬁc time trends in Figure 2B,
the estimated coefﬁcient became smaller in magnitude
and not statistically signiﬁcant beginning in the second
year of expansions. It is important to note that state-spe-
ciﬁc time trends may reﬂect the effect of the policy and
not just pre-existing trends.
In this sense, the inclusion
of these trends may lead to conservative estimates of the
effects of expansions. However, the authors chose to be
conservative in this set of analyses.
Table 1, Columns 2−6 and Appendix Figure F1
(available online) present estimates for new HIV diagno-
ses separately by transmission category. Owing to the
high number of censored cells in these subgroups at the
county level, the estimated coefﬁcients by risk category
were conducted at the state level. These results suggest
that Medicaid expansions were associated with a statisti-
cally signiﬁcant increase in the rate of infections attrib-
uted to injection drug use as well as male-to-male sexual
contact and injection drug use. However, there were no
detectable effects in other categories.
Appendix Figure F2, available online, considers the
extent to which Medicaid expansions affected various
county subgroups. These analyses reveal that the
increase in new diagnoses was concentrated in rural
counties, counties with high poverty rates, and counties
with high rates of pre-ACA uninsured. Appendix Figure
F3, available online, suggests that increases in HIV
Table 1. Changes in New Diagnoses Among Counties That Expanded Medicaid Relative to Those That Did Not
sexual contact and
injection drug use Other
(1) (2) (3) (4) (5) (6)
Expansion 0.508** (0.238) 0.485 (0.405) 0.351*** (0.099) 0.572 (0.636) 0.133*(0.078) 0.006 (0.006)
Pre-2014 mean 3.659 3.116 0.720 6.742 0.440 0.022
Observations 25,064 408 408 408 408 408
Note: The table shows the results of the difference-in-differences regressions using HIV diagnoses from CDC. Column (1) uses new cases per
100,000 individuals by county from 2010 to 2017; Columns (2) through (6) use the new cases per 100,000 individuals by state from 2010 to 2017.
The regressions control for unemployment rate, percentage of population that is male, aged 25‒44 years, Hispanic, and Black; policy variables that
account for opioid prescription limits, prescription drug monitoring programs, and other requirements to prevent illicit opioid-seeking behavior; and
state and year ﬁxed effects. SEs in parentheses are clustered at the state level. Boldface indicates statistical signiﬁcance (
CDC, Centers for Disease Control and Prevention.
338 Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
diagnoses were statistically signiﬁcant only in the Mid-
west and Southern counties. Although this graph reﬂects
a pre-existing downward trend in the Northeast, this
trend disappeared when states that partially expanded
their program before 2014 were dropped.
The analysis of the effects of Medicaid expansions on
knowledge of status appears in Table 2 and shows a sig-
niﬁcant increase as a function of the expansions (Table 2,
Column 1 and Appendix Figure F4, available online).
This increase was more pronounced in states with a high
rural population, high poverty rates, and high rates of
pre-ACA uninsured (Appendix Figure F5, available
Table 2, Column 2 suggests that Medicaid expansions
increased the number of PrEP users by 2.643 per
100,000 population (p<0.10). Although this average
increase in PrEP use in expansion states compared with
nonexpansion states was not signiﬁcant at p<0.05,
Appendix Figure F6, available online, shows a lagged
increase in PrEP use starting from the second year of the
expansion, which is consistent with the increasing dis-
semination of PrEP as a new prevention method. These
increases were larger in counties with the highest pre-
ACA uninsured rates and urban areas, suggesting that
there are additional barriers in PrEP uptake in rural
areas (Appendix Figure F7, available online).
Consistent with the predictions from Figure 1, the effect
of the Medicaid expansions on HIV incidence was not sta-
tistically signiﬁcant (Table 2,Column3andAppendix
Figure F8, available online). In addition, the rates of chla-
mydia, gonorrhea, and syphilis were analyzed (Table 2,
Columns 4−7andAppendix Figure F9, available online).
The rates of gonorrhea decreased after Medicaid expan-
sions; however, there was no signiﬁcant change in the diag-
noses of chlamydia or syphilis. Finally, there was no
evidence that Medicaid expansions changed any opioid-
related outcomes (Table 2,Columns8−10 and Appendix
The National HIV/AIDS Strategy includes the goal of
reducing the social health disparities that continue to
deﬁne the HIV epidemic. Although many social factors
contribute to these disparities, health insurance accounts
for much of the variation in access to care,
that insurance expansions should have important impli-
cations for HIV prevention. This study examined how
the ACA Medicaid expansions affected HIV diagnoses
and PrEP use.
Results of DID models indicated that the expan-
sions increased HIV diagnoses. However, only the
increase in the ﬁrst year of the expansion was robust
to alternative model speciﬁcations. Appendix E,avail-
able online, presents the DID estimated coefﬁcient
for the likelihood of HIV testing. Consistent with the
literature, expansions in Medicaid eligibility were
associated with increases in HIV testing.
increase in HIV diagnoses indicates that improved
access to HIV testing resulting from insurance expan-
HIV who are aware of their status.
Figure 2. Changes in HIV diagnoses among counties that expanded Medicaid relative to counties that did not, by time period rela-
tive to expansion. (A) Without control for state-speciﬁc linear time trends. (B) Including state-speciﬁc linear time trends.
Notes: The ﬁgures show point estimates (and 95% CIs) from regression of HIV diagnoses in each county on a series of indicator variables for time rel-
ative to the expansion of Medicaid eligibility. Estimated rates of new diagnoses during the year before expansion were omitted. Data on new HIV
cases per 100,000 individuals by county came from CDC for 2010‒2017. The regressions control for unemployment rate, percentage of population
that is male, aged 25‒44 years, Hispanic, and Black; policy variables that account for opioid prescription limits, prescription drug monitoring pro-
grams, and other requirements to prevent illicit opioid-seeking behavior; and state and year ﬁxed effects. (B) includes state-speciﬁc linear time
trends. SEs are clustered at the state level. CDC, Centers for Disease Control and Prevention.
Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
Another ﬁnding is that although the rate of infections
attributed to injection drug use increased, the rates of
infections transmitted through male-to-male or hetero-
sexual contact remained stable. This ﬁnding is not sur-
prising as the U.S. is in the midst of an opioid crisis, and
the increase in injection drug use has led to a greater risk
of illness owing to needle sharing. Because there is no
evidence that Medicaid expansions affected substance
use, the increase in HIV diagnoses attributed to injection
is consistent with the improved access to care among
those with substance use disorder. Moreover, people
with substance use disorders were more likely to be
uninsured before the ACA than the general
In addition, this study suggests that Medicaid expan-
sions were associated with greater use of PrEP and
reductions in the prevalence of gonorrhea. The reduc-
tion in the rate of gonorrhea is consistent with the
improved access to care after the expansions, because
detection of gonorrhea typically leads to a rapid resolu-
tion of the STI and may decrease its prevalence.
This study has important policy implications. Ongo-
ing debates have focused, in part, on the role of access to
affordable healthcare plans on disproportionate HIV
This study was ideally suited to answer this
question by providing evidence that Medicaid expan-
sions were associated with improvements in several
HIV-related outcomes. Thus, nonexpanding states, espe-
cially the Southern states where incidence is currently
could facilitate HIV prevention by extending
insurance coverage to low-income residents.
Limitations of these analyses include the lack of access to
unsuppressed data on HIV diagnoses. Although the sen-
sitivity analysis suggested that the qualitative conclu-
sions of the study remained robust to alternative
imputation methods, the magnitude of the effects was
affected. Also, as with any quasi-experimental method,
these analyses are subject to potential time-varying con-
founders, although pre-expansion trends did not differ
between expansion and nonexpansion states.
The study ﬁndings suggest that expanding eligibility for
Medicaid increased HIV diagnoses. This ﬁnding is impor-
tant because CDC estimates show that a high fraction of
new HIV infections in the U.S. in 2016 were transmitted
by people who were living with HIV but were not aware of
This study provides important evidence sug-
gesting that increasing health insurance coverage may play
a critical role in eradicating HIV.
Table 2. Changes in Outcomes Among Counties That Expanded Medicaid Relative to Those That Did Not
incidence Chlamydia Gonorrhea Syphilis
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
(0.842) 2.643*(1.537) 0.703 (0.941) ‒8.972 (8.600) ‒5.701
(2.894) ‒0.436 (0.293) ‒0.476 (0.286) ‒0.061 (0.191) 6.269 (5.218) 1.198 (1.191)
Pre-2014 mean 81.646 4.147 11.458 353.537 75.396 2.296 2.498 7.862 24.170 16.947
Observations 347 18,798 343 25,063 25,062 24,950 24,678 408 98 249
Note: The table shows the results of the difference-in-differences regressions. Column (1) is based on CDC data on the awareness of HIV status by state from 2010 to 2017, Column (2) uses county-
level data on the number of PrEP users per 100,000 individuals from the AIDSVu for 2012‒2017, Column (3) estimate is based on CDC data on HIV incidence per 100,000 population per state for
2010‒2017, Columns (4) to (7) use the CDC data on STI counts per 100,000 individuals by county from 2010 to 2017, Column (8) is based on CDC data on rates of opioid prescriptions dispensed by
state from 2010 to 2017, and Columns (9) and (10) use HCUP data on rates of opioid-related hospital visits by state from 2010 to 2016. The regressions control for unemployment rate, percentage of
population that is male, aged 25‒44 years, Hispanic, and Black; policy variables that account for opioid prescription limits, prescription drug monitoring programs, and other requirements to prevent
illicit opioid-seeking behavior; and state and year ﬁxed effects. SEs in parentheses are clustered at the state level. Boldface indicates statistical signiﬁcance (
CDC, Centers for Disease Control and Prevention; ER, emergency room; HCUP, Healthcare Cost and Utilization Program; PrEP, pre-exposure prophylaxis; STI, sexually transmitted infection.
340 Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
Research reported in this publication was supported by the
National Institute of Allergy and Infectious Diseases of NIH
(grant number R01AI147487), the National Institute of Mental
Health (grant number R01MH114847), and the National Insti-
tute on Drug Abuse (award number DP1 DA048570). The con-
tent is solely the responsibility of the authors and does not
necessarily represent the ofﬁcial views of NIH.
No ﬁnancial disclosures or conﬂicts of interest were reported
by the authors of this paper.
Supplemental materials associated with this article can be
found in the online version at https://doi.org/10.1016/j.
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