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Effect of Medicaid Expansions on HIV Diagnoses and Pre-Exposure Prophylaxis Use

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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 these effects. 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 mechanisms addressed associations with HIV incidence, rates of sexually transmitted infections, and substance 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 living 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.
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RESEARCH ARTICLE
Effect of Medicaid Expansions on HIV Diagnoses and
Pre-Exposure Prophylaxis Use
Bita Fayaz Farkhad, PhD,
1
David R. Holtgrave, PhD,
2
Dolores Albarracín, PhD
1
Introduction: Increased insurance coverage and access to health care can increase identication 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
these effects.
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 rst 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 preAffordable 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.
INTRODUCTION
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
From the
1
Department of Psychology, University of Illinois at Urbana-
Champaign, Champaign, Illinois; and
2
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: bitaf@illinois.edu.
0749-3797/$36.00
https://doi.org/10.1016/j.amepre.2020.10.021
© 2020 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights
reserved.
Am J Prev Med 2021;60(3):335342 335
populations with signicant 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)
13
for eligible low-income individ-
uals through Medicaid? Although prior studies have
identied a positive impact of Medicaid expansions on
HIV testing,
4,5
answers to these 2 specic research ques-
tions can offer important insights for implementing the
current national HIV prevention strategy and fulll the
commitment to end the spread of HIV in the U.S. by
2030.
The benets of an early HIV diagnosis and PrEP are
broad and well known. An HIV diagnosis, currently
made an average of 3 years after infection,
6
is the precur-
sor of access to antiretroviral therapy, which also pre-
vents death and morbidity and the spread of the virus.
6
11
Furthermore, an HIV diagnosis reduces risk behav-
ior among people who were previously undiagnosed,
12
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.
13
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.
13
However, even
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.
1417
Likewise, PrEP is
paid for by state Medicaid programs.
14,15,18
Hence, the
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.
15,16
Second, having health
insurance may increase coverage of substance use treat-
ment. Substance use treatment has been shown to facili-
tate HIV diagnoses
19,20
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 gure 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
342
www.ajpmonline.org
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
difcult 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 reect
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 reect 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,
21,22
this study is the rst 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.
METHODS
Study Sample
Statesstatus regarding Medicaid expansion was obtained from
the Kaiser Family Foundation.
23
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 denitions of each variable.
Measures
The HIV diagnoses per 100,000 population for each county-year
came from CDCs National Center for HIV/AIDS, Viral Hepatitis,
STD (sexually transmitted disease) and TB (tuberculosis) Atlas
from 2010 to 2017.
24
The data on HIV diagnoses are also classied
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
condentiality of personally identiable 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.
25
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 20102017.
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 20122017.
26
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 20102017. 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 20102016 Healthcare Cost and Utilization Program.
27
The models also included county-level measures of demo-
graphics, including the fraction of county population that is male
and aged 2544 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.
28
to ensure that the estimates were not con-
founded by other simultaneous drug-related policy changes.
Statistical Analysis
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 coefcient 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-
specic linear time trends were added to some of the specications
Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
342 337
March 2021
to additionally control for state-specic 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
level.
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.
29
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.
RESULTS
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 signicant 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
indicator variable.
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-specic 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 signicant. At the time of the
Medicaid expansion, the difference in HIV diagnoses
between expansion and nonexpansion states increased
and remained above the earlier coefcients in Years 2
and 3 (Figure 2A). Although the DID coefcient esti-
mated in the year following the expansion was robust to
the inclusion of state-specic time trends in Figure 2B,
the estimated coefcient became smaller in magnitude
and not statistically signicant beginning in the second
year of expansions. It is important to note that state-spe-
cic time trends may reect the effect of the policy and
not just pre-existing trends.
30
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 26 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 coefcients by risk category
were conducted at the state level. These results suggest
that Medicaid expansions were associated with a statisti-
cally signicant 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
Variable
All diagnoses
Heterosexual
contact
Injection
drug use
Male-to-male
sexual contact
Male-to-male
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 2544 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 xed effects. SEs in parentheses are clustered at the state level. Boldface indicates statistical signicance (
*p<0.1;
**p<0.05;
***p<0.01).
CDC, Centers for Disease Control and Prevention.
338 Fayaz Farkhad et al / Am J Prev Med 2021;60(3):335
342
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diagnoses were statistically signicant only in the Mid-
west and Southern counties. Although this graph reects
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-
nicant 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
online).
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 signicant 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 signicant (Table 2,Column3andAppendix
Figure F8, available online). In addition, the rates of chla-
mydia, gonorrhea, and syphilis were analyzed (Table 2,
Columns 47andAppendix Figure F9, available online).
The rates of gonorrhea decreased after Medicaid expan-
sions; however, there was no signicant change in the diag-
noses of chlamydia or syphilis. Finally, there was no
evidence that Medicaid expansions changed any opioid-
related outcomes (Table 2,Columns810 and Appendix
Figure F10,availableonline).
DISCUSSION
The National HIV/AIDS Strategy includes the goal of
reducing the social health disparities that continue to
dene the HIV epidemic. Although many social factors
contribute to these disparities, health insurance accounts
for much of the variation in access to care,
31
suggesting
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 rst year of the expansion was robust
to alternative model specications. Appendix E,avail-
able online, presents the DID estimated coefcient
for the likelihood of HIV testing. Consistent with the
literature, expansions in Medicaid eligibility were
associated with increases in HIV testing.
21
The
increase in HIV diagnoses indicates that improved
access to HIV testing resulting from insurance expan-
sionsincreasedthepercentageofpeoplelivingwith
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-specic linear time trends. (B) Including state-specic linear time trends.
Notes: The gures 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 20102017. The regressions control for unemployment rate, percentage of population
that is male, aged 2544 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 xed effects. (B) includes state-specic 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
342 339
March 2021
Another nding 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 nding 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
population.
32
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.
33
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
burden.
34
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
highest,
35
could facilitate HIV prevention by extending
insurance coverage to low-income residents.
Limitations
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.
CONCLUSIONS
The study ndings suggest that expanding eligibility for
Medicaid increased HIV diagnoses. This nding 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
their status.
36
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
Variable
Awareness of
HIV status
PrEP
users
HIV
incidence Chlamydia Gonorrhea Syphilis
Latent
syphilis
Prescription
opioids
Opioid-
related
ER visits
Opioid-
related
inpatient
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Expansion 1.798
**
(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 20122017, Column (3) estimate is based on CDC data on HIV incidence per 100,000 population per state for
20102017, 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 2544 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 xed effects. SEs in parentheses are clustered at the state level. Boldface indicates statistical signicance (
*p<0.1;
**p<0.05).
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
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ACKNOWLEDGMENTS
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 ofcial views of NIH.
No nancial disclosures or conicts of interest were reported
by the authors of this paper.
SUPPLEMENTAL MATERIAL
Supplemental materials associated with this article can be
found in the online version at https://doi.org/10.1016/j.
amepre.2020.10.021.
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www.ajpmonline.org
... Previous literature supports that Medicaid expansions (as well as coverage via private insurance expansions) increase the frequency of regular checkups (Sommers et al., 2017) and HIV screenings (Katz & Jha, 2019). Even though the added health coverage can reduce gonorrhea rates, coverage does not have a significant impact on HIV incidence nor on diagnoses of syphilis or chlamydia (Farkhad et al., 2021). However, there is (limited) evidence that Medicaid expansions cause ex ante moral hazard by lowering the relative risk of sexual activity. ...
... Eilam and Delhommer (2020) show that one additional male PrEP user increases male syphilis, gonorrhea, and chlamydia cases. Since Medicaid expansions increase the number of people with health insurance and they increase utilization of health care services (Lee, 2018), including the use of PrEP (Farkhad et al., 2021), health insurance can have an effect on uptake of prescriptions for those drugs. Therefore, as part of the heterogeneity analysis I investigate whether greater utilization of antiretroviral drugs (HAART, PrEP) leads to differential changes in STI rates for states that have legalized same-sex marriage. ...
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Even though prior research has investigated the relationship between same‐sex partnership recognition policies and health outcomes, the impact of same‐sex marriage laws on sexually transmitted infections has not received much attention. Using state‐level panel data from 2000 to 2019, I show that marriage equality legislation decreases the spread of (shorter‐term) syphilis infections and of (longer‐term) human immunodeficiency virus (HIV) and acquired immunodeficiency syndrome (AIDS) infections among the general population. Event study analyses correcting for non‐staggered treatment implementation confirm these negative effects, but also suggest that standard difference‐in‐differences models understate the impact of the legislation by up to 8% points. Further analysis supports that these legislation effects operate through three mechanisms: increasing social tolerance, strengthening relationship commitment, and expanding health care access and coverage for HIV/AIDS prevention and treatment. Disaggregating the results by sexual behavior reveals that legal access to same‐sex marriage leads to sizable decreases in AIDS rates among men who have sex with men (MSM) (the most at‐risk population for an infection). Even though there is economically significant evidence that the legislation improves sexual health of the heterosexual population due to increased utilization of preventive sexual health care, the legislation does not have a direct impact on infection rates for the non‐MSM population.
... The expansion, which now extends to 38 states, has been associated with modest improvements in PrEP access. 47 Medicaid programs are entitled to statutorily mandated discounts through the Medicaid Drug Rebate Program and have also been able to secure supplemental discounts offered by manufacturers, which have helped to facilitate access to PrEP medications. ...
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The U.S. has the tools to end the HIV epidemic, but progress has stagnated. A major gap in U.S. efforts to address HIV is the under-utilization of medications that can virtually eliminate acquisition of the virus, known as pre-exposure prophylaxis (PrEP). This document proposes a financing and delivery system to unlock broad access to PrEP for those most vulnerable to HIV acquisition and bring an end to the HIV epidemic.
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Research is limited on the effect of racism and social determinants of health on HIV pre-exposure prophylaxis (PrEP) use. This study used the PrEP-to-Need Ratio (PNR), which measures PrEP prescriptions divided by HIV diagnoses in the county, to evaluate sufficient PrEP use. AIDSVu datasets were compared to county-level social determinants of health. Standardized regression coefficients (β) were compared to identify strongest associations with PNR. Overall, factors including percent African American and percent uninsured had negative correlations with PNR, whereas median household income and severe housing cost burden had positive associations. Stratifying for population size, percent African American, percent uninsured, and severe housing cost burden were significant for low population areas, whereas median household income, percent in poverty, percent uninsured, and percent African American were significant for large populations. To reduce PrEP disparities, public health must develop strategies to reach those most in need, especially historically disadvantaged communities.
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This study investigated the association between interest in Pre-exposure Prophylaxis (PrEP) in the US using Google Health Trends as a source of big data and state policy variables of Medicaid expansions under the Affordable Care Act (ACA) and initiation of PrEP Assistance Programs (PrEP-AP). As of December 2019, thirty-three states and the District of Columbia have accepted federal Medicaid funding provided through the ACA to expand eligibility to low-income adults. Among these expansion states, eight states also implemented PrEP-AP, a program that finances PrEP. A difference-in-differences approach estimated how changes in Google search for PrEP before and after the expansion differed across expansion and non-expansion states. Analyses also gauged whether the magnitude of the correlation between Medicaid expansions and Google searches was higher in states that also initiated PrEP-AP. Findings indicated that the Medicaid expansions were associated with a higher share of Google searches for PrEP keywords (β=1.536, S.E. =.36, p<.001). Moreover, the magnitude of correlation for some keywords was higher in states that also implemented PrEP-APs.
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It has been nearly 4 decades since the Centers for Disease Control and Prevention reported a rare lung infection among 5 previously healthy young men in Los Angeles—what would be the first recorded cases of Pneumocystis carinii pneumonia in men who were discovered to have human immunodeficiency virus (HIV) infection. Since the first cases of AIDS were identified in the United States, the number of people with HIV in the United States has reached an estimated 1.2 million, with nearly 40 000 people receiving a new diagnosis in 2017 alone.¹ This infection, which was initially nearly uniformly fatal, has become a chronic disease largely because of the scientific breakthrough of a new group of medications, known as highly active antiretroviral therapy (HAART), which has helped to control the epidemic in the United States and globally. Despite this pivotal advancement, only 60% of the people living with HIV in the United States have achieved viral suppression.¹ Additionally, the financial costs of HIV are substantial, especially for the federal government, which spent an estimated $20 billion on HIV care and treatment in fiscal year 2016 alone.² Even though the human and financial burdens of HIV remain substantial, it is now possible to end transmission of the virus and control the epidemic in the United States within the next 10 years.
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In 2016, the Prevention Access Campaign, a health equity initiative with the goal of ending the HIV/AIDS pandemic as well as HIV-related stigma, launched the Undetectable = Untransmittable (U = U) initiative.¹ U = U signifies that individuals with HIV who receive antiretroviral therapy (ART) and have achieved and maintained an undetectable viral load cannot sexually transmit the virus to others. This concept, based on strong scientific evidence, has broad implications for treatment of HIV infection from a scientific and public health standpoint, for the self-esteem of individuals by reducing the stigma associated with HIV,² and for certain legal aspects of HIV criminalization.³ In this Viewpoint, we examine the underlying science-based evidence supporting this important concept and the behavioral, social, and legal implications associated with the acceptance of the U = U concept.
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Purpose: This short communication examines the impact of the Patient Protection and Affordable Care Act (PPACA) on insurance coverage and substance use treatment access among persons with opioid use disorders. Methods: Data came from the 2010-2015 National Surveys on Drug Use and Health. Among persons with heroin and opioid pain-reliever use disorders, measures of insurance coverage and treatment access were compared before and after the implementation of major PPACA provisions that expanded access to insurance in 2014. Results: The prevalence of uninsured persons among those with heroin use disorders declined dramatically following PPACA implementation (OR 0.59, 95% CI 0.39-0.89), largely due to an increase in the prevalence of Medicaid coverage (OR 1.96, 95% CI 1.21-3.18). There was no evidence of an increase in the prevalence of treatment, but among persons who received treatment, there was an increase in the proportion whose treatment was paid for by insurance (OR 3.75, 95% CI 2.13-3.18). By contrast, there was no evidence the uninsured rate declined among persons with pain-reliever use disorders. Conclusions: The PPACA Medicaid expansion increased insurance coverage among persons with heroin use disorders, and likely plays an essential role in protecting the health and financial security of this high-risk group. More research is needed on the relationship between insurance acquisition and utilization of substance use treatment.
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We examined the effect of the expansion of Medicaid eligibility under the Affordable Care Act on health insurance coverage and labor supply of low-educated and low-income adults. We found that the Medicaid expansions were associated with large increases in Medicaid coverage, for example, 50 percent among childless adults, and corresponding decreases in the proportion uninsured. There was relatively little change in private insurance coverage, although the expansions tended to decrease such coverage slightly. In terms of labor supply, estimates indicated that the Medicaid expansions had little effect on work effort despite the substantial changes in health insurance coverage. Most estimates suggested that the expansions increased work effort, although not significantly.