Abstract
Objectives. To compare health care coverage and utilization between men who have sex with men (MSM) in Medicaid expansion versus nonexpansion states.
Methods. We used cross-sectional weighted data from the National HIV Behavioral Surveillance system, which used venue-based methods to interview and test MSM in 22 US cities from June through December, 2017 (n = 8857). We compared MSM in Medicaid expansion versus nonexpansion states by using the Rao-Scott χ2 test stratified by HIV status. We used multivariable logistic regression to model the relationship between Medicaid expansion, coverage, and preexposure prophylaxis (PrEP) use.
Results. MSM in expansion states were more likely to have insurance (87.9% vs 71.6%), have Medicaid (21.3% vs 3.8%), discuss PrEP with a provider (58.8% vs 44.3%), or use PrEP (31.1% vs 17.5%).
Conclusions. Medicaid expansion is associated with higher coverage and care, including PrEP.
Public Health Implications. States may consider expanding Medicaid to help end the HIV epidemic.
As of 2017, 37 states and Washington, DC, have expanded Medicaid as part of the Patient Protection and Affordable Care Act (ACA),1 extending eligibility to nonelderly adults with incomes less than or equal to 138% of the federal poverty level (FPL). Medicaid expansion increased health care coverage for many populations, including sexual minorities.2
Pre- and postexpansion analyses found increases in health care coverage and having a usual source of care2 among gay, bisexual, and other men who have sex with men (MSM). Despite those gains, many MSM live in states that did not expand Medicaid. Among the approximately 4.4 million uninsured adults who would have been eligible had their state expanded Medicaid, most live in the South,3 where new HIV diagnoses and racial/ethnic disparities are high. Populations at risk for HIV are disproportionately low income and likely to be eligible under the expansion criteria.4 A previous analysis found that Medicaid expansion was associated with health care access and utilization among persons who inject drugs.5 However, it is unknown whether MSM experience differences in health care coverage or utilization in expansion versus nonexpansion states.
To achieve the national goal of ending the HIV epidemic,6 it is critical to increase use of preexposure prophylaxis (PrEP),7 a daily pill that is about 99% effective in preventing HIV.8 Health care access is important to ensure that people with HIV engage in care, visit their provider regularly, and achieve viral suppression, which helps patients stay healthy and have effectively no risk of transmitting HIV.
PrEP’s effectiveness depends on adherence,9 but cost can be a barrier.10 Without insurance or assistance, PrEP can cost thousands of dollars per year in out-of-pocket expenses. Although pharmaceutical companies offer assistance programs, patients sometimes experience gaps in assistance,11 which could affect adherence. Although Medicaid and most private plans already cover PrEP, the US Preventive Services Task Force classified PrEP as a grade A medication, requiring plans to cover it without cost sharing in 2021.12 However, low-income MSM in nonexpansion states may not have access because of stricter Medicaid eligibility criteria.13 MSM in nonexpansion states who can neither afford private insurance nor qualify for Medicaid may be vulnerable.
We compared MSM in states that expanded versus did not expand Medicaid, stratified by HIV status.
METHODS
The Centers for Disease Control and Prevention’s (CDC’s) National HIV Behavioral Surveillance (NHBS) system collects cross-sectional data on HIV-related behaviors among populations at risk for HIV, including MSM.14 We recruited MSM through a venue-based sampling methodology for interviews and HIV testing in 23 US cities from June through December 2017. We selected cities based on highest HIV prevalence. NHBS sampling procedures have been previously published.15
We limited our analysis to men who had sex with another man in the past 12 months, were 18 to 64 years old because of near-universal Medicare access for persons 65 years old or older, lived in a participating metropolitan statistical area, were able to complete the interview in English or Spanish, and had a valid HIV test result. We excluded data from San Juan, Puerto Rico, because their Medicaid system was not comparable to other project areas.16 Of 13 852 people screened, we included 8857.
We weighted NHBS data to account for unequal selection probabilities, multiplicity, and nonresponse bias, allowing us to extrapolate to all venue-attending MSM.
Definitions
We defined Medicaid expansion status as implementing Medicaid expansion before June 1, 2017.1 Table 1 shows states’ Medicaid expansion status, the year the state implemented the policy, and NHBS project areas. Because metropolitan statistical areas may cross state borders, we classified participants by state of residence. Because states expanded at different times, we calculated time since expansion as months since implementation.
TABLE 1—
Map of MSAs, States’ Medicaid Expansion Status, and Implementation: United States, National HIV Behavioral Surveillance, 2017
| NHBS MSAa | Medicaid Expansion Status | Year of Expansion |
| CA | ||
| Los Angeles | Expanded | 2014 |
| San Francisco | Expanded | 2014 |
| San Diego | Expanded | 2014 |
| CO: Denver | Expanded | 2014 |
| FL: Miami | Did not expand | . . . |
| GA: Atlanta | Did not expand | . . . |
| IL: Chicago | Expanded | 2014 |
| LA: New Orleans | Expanded | 2016 |
| MA: Boston | Expanded | 2014 |
| MD: Baltimore | Expanded | 2014 |
| MI: Detroit | Expanded | 2014 |
| NJ: Newark | Expanded | 2014 |
| NY | ||
| Nassau-Suffolk | Expanded | 2014 |
| New York City | Expanded | 2014 |
| OR: Portland | Expanded | 2014 |
| PA: Philadelphia | Expanded | 2015 |
| PR: San Juan | Excluded from analysis | . . . |
| TN: Memphis | Did not expand | . . . |
| TX | ||
| Dallas | Did not expand | . . . |
| Houston | Did not expand | . . . |
| VA: Norfolk | Did not expand | . . . |
| WA: Seattle | Expanded | 2014 |
| Washington, DC | Expanded | 2014 |
Note. MSA = metropolitan statistical area. NHBS = National HIV Behavioral Surveillance. Massachusetts enacted a similar health care reform policy in 2006.
Source. State Medicaid status was categorized using Kaiser Family Foundation’s interactive map as of June 1, 2017: https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map.
Some metropolitan statistical areas extended into multiple states. Participants were categorized based on state of residence, regardless of the city they were sampled in. Participants sampled in Baltimore, MD; Norfolk, VA; or Washington, DC, may have lived in MD, VA, or Washington, DC. Memphis, TN, participants may have lived in AR, MS, or TN. Participants sampled in Newark, NJ; Nassau-Suffolk, NY; or New York, NY, may have lived in NJ or NY. Portland, OR participants may have lived in OR or WA.
We assessed poverty using the 2017 Health and Human Services guidelines based on household income and number of dependents.17 To determine differences in possible Medicaid eligibility, we categorized household income as less than 100%, 100% to 138%, and greater than 138% of the FPL. We defined insurance status as currently having any type of health insurance.
We limited all PrEP variables to HIV-negative MSM who were aware of PrEP. We limited discussion of PrEP with a provider in the past 12 months to MSM who visited any provider in the past 12 months. We measured PrEP use as taking PrEP in the past 12 months. The full NHBS questionnaire is available online.18
Analysis
We obtained weighted percentages and 95% confidence intervals (CIs). We compared characteristics between MSM living in expansion states versus nonexpansion states by using the Rao-Scott χ2 test (P < .05). Because people with HIV often have access to other forms of assistance, such as the Ryan White HIV/AIDS Program, we also stratified results by HIV status. We excluded or suppressed variables with an unstable coefficient of variation (CV) because of sparse data (CV > 0.30).
Then, we used multivariable logistic regression models to assess how state Medicaid expansion policy was related to 3 outcomes: current insurance status, current Medicaid status, and PrEP use in the past 12 months. We estimated crude and adjusted prevalence ratios (APRs) and 95% CIs. We selected covariates for each model based on the literature or a priori interest.
The model examining the association between Medicaid expansion and current insurance status controlled for race/ethnicity, age, employment, income, HIV status, geographic region, and time since expansion.
The model measuring the association between expansion status and current Medicaid status controlled for race/ethnicity, age, employment, income, disability, HIV status, geographic region, and time since expansion. We categorized the Medicaid status outcome as MSM with Medicaid versus any other insurance, excluding uninsured MSM.
We modeled the association between state Medicaid expansion and PrEP use among HIV-negative MSM, controlling for race/ethnicity, age, current insurance status, geographic region, and time since expansion. We included disclosing sexuality and discussing PrEP with a provider, as they are related to obtaining a prescription.19
We conducted analyses using SAS version 9.4 (SAS Institute, Cary, NC) and SUDAAN version 11.0.3 (RTI International, Research Triangle Park, NC).
RESULTS
Overall (n = 8857), 28.3% of MSM lived in nonexpansion states. Compared with MSM in expansion states (Table 2), men who lived in nonexpansion states were more likely to be non-Hispanic Black (hereafter referred to as Black; 35.3% vs 21.5%) or Hispanic/Latino (35.3% vs 30.1%). Among MSM in nonexpansion states, 14.8% and 9.0% had incomes within 0% to 100% and 100% to 138% of the FPL, respectively, and may have been eligible for Medicaid had they lived in an expansion state.
TABLE 2—
Sociodemographic and Care Differences Between MSM Living in Medicaid Expansion Versus Nonexpansion States: United States, National HIV Behavioral Surveillance, 2017
| Did Not Expand Medicaid (n = 2507) |
Expanded Medicaid (n = 6350) |
||||
| Variable | No. | %a (95% CI) | No. | %a 95% CI | P |
| Race/ethnicityb | < .001 | ||||
| Non-Hispanic Black | 976 | 35.3 (30.6, 39.9) | 1729 | 21.5 (19.2, 23.8) | |
| Hispanic/Latino | 793 | 35.3 (31.5, 39.2) | 1403 | 30.1 (27.9, 32.2) | |
| Non-Hispanic White | 597 | 24.1 (20.6, 27.5) | 2536 | 38.6 (36.2, 41.0) | |
| Other/multiracial | 133 | 5.3 (3.8, 6.8) | 642 | 9.8 (8.8, 10.8) | |
| Age, y | .06 | ||||
| 18–29 | 1099 | 45.5 (41.5, 49.5) | 2486 | 40.8 (38.3, 43.3) | |
| 30–39 | 712 | 28.4 (25.9, 30.8) | 1983 | 32.8 (30.8, 34.7) | |
| 40–49 | 370 | 14.4 (12.3, 16.5) | 984 | 14.5 (13.0, 15.9) | |
| 50–64 | 326 | 11.7 (9.4, 14.0) | 897 | 12.0 (10.4, 13.6) | |
| Employment | .005 | ||||
| Employed full/part time | 2058 | 84.0 (82.1, 86.0) | 4894 | 79.8 (78.1, 81.4) | |
| Not in labor force/cannot work | 251 | 9.0 (7.4, 10.5) | 760 | 11.1 (9.9, 12.3) | |
| Unemployed | 198 | 7.0 (5.7, 8.3) | 695 | 9.2 (7.9, 10.5) | |
| Povertyd | .72 | ||||
| < 100% FPL | 419 | 14.8 (12.5, 17.2) | 1220 | 15.9 (14.1, 17.7) | |
| 100%–138% FPL | 260 | 9.0 (7.4, 10.6) | 571 | 9.2 (8.9, 10.4) | |
| ≥ 139% FPL | 1808 | 76.2 (73.5, 78.8) | 4513 | 74.9 (72.8, 77.0) | |
| Homeless, 12 mo | .12 | ||||
| Yes | 166 | 6.0 (4.5, 7.5) | 602 | 7.5 (92.5, 95.5) | |
| No | 2341 | 94.0 (92.5, 95.5) | 5748 | 92.5 (91.4, 93.6) | |
| HIV status | .01 | ||||
| HIV positive | 676 | 24.9 (21.8, 27.9) | 1442 | 20.4 (18.4, 22.4) | |
| HIV negative | 1831 | 75.2 (72.1, 78.2) | 4908 | 79.6 (77.6, 81.7) | |
| Any disability | .33 | ||||
| Yes | 482 | 17.3 (15.2, 19.5) | 1341 | 18.7 (17.1, 20.2) | |
| No | 2023 | 82.7 (80.5, 84.8) | 4995 | 81.3 (79.8, 82.9) | |
| Currently insured | < .001 | ||||
| Yes | 1777 | 71.6 (68.7, 74.4) | 5557 | 87.9 (86.6, 89.2) | |
| No | 727 | 28.4 (25.6, 31.3) | 782 | 12.1 (10.8, 13.4) | |
| Insurance typec | < .001 | ||||
| Private only | 1263 | 56.0 (52.7, 59.3) | 3377 | 56.2 (54.0, 58.5) | |
| Medicaid only | 128 | 3.8 (2.8, 4.8) | 1418 | 21.3 (19.5, 23.1) | |
| Medicare only | 57 | 1.3 (0.8, 1.8) | 155 | 1.6 (1.1, 2.2) | |
| Other/multiple types | 326 | 10.4 (8.7, 12.2) | 593 | 8.7 (7.7, 9.7) | |
| No insurance | 727 | 28.5 (25.7, 31.3) | 782 | 12.1 (10.8, 13.4) | |
| Usual source of care | < .001 | ||||
| Yes | 1933 | 78.9 (76.2, 81.5) | 5362 | 85.9 (84.6, 87.3) | |
| No | 554 | 21.1 (18.5, 23.8) | 930 | 14.1 (12.7, 15.4) | |
| Health care visit, 12 moc | < .001 | ||||
| Yes | 2074 | 81.7 (79.2, 84.1) | 5564 | 87.7 (86.4, 89.0) | |
| No | 431 | 18.3 (15.9, 20.8) | 785 | 12.3 (11.0, 13.6) | |
| Disclosed sexual identity to providerc | < .001 | ||||
| Yes | 2027 | 80.7 (78.3, 83.2) | 5470 | 86.7 (85.3, 88.1) | |
| No | 477 | 19.3 (16.8, 21.7) | 875 | 13.3 (12.0, 14.7) | |
Note. CI = confidence interval; FPL = federal poverty limit; MSM = men who have sex with men; NHBS = National HIV Behavioral Surveillance. Study size was n = 8857. Expansion states were those that implemented Medicaid expansion before June 1, 2017.
Column percentages were weighted; not all percentages sum to 100 because of missing or suppressed values; values suppressed if coefficient of variation was > 0.30.
Hispanic/Latinos could be of any race; all racial groups were single-race; other racial groups were American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, and multiracial.
Poverty defined by 2017 Department of Health and Human Services federal poverty guidelines: https://www.federalregister.gov/documents/2017/01/31/2017-02076/annual-update-of-the-hhs-poverty-guidelines.
MSM in nonexpansion states were more likely to be uninsured than were MSM in expansion states (28.4% vs 12.1%). They were less likely to have Medicaid (3.8% vs 21.3%), a usual source of care (78.9% vs 85.9%), visited a provider in the past 12 months (81.7% vs 87.7%), or disclosed their sexuality to a provider (80.7% vs 86.7%).
Because most states that did not expand Medicaid were in the South, we compared key variables between New Orleans, Louisiana, which expanded Medicaid, and other Deep South cities, which had not (Table A [available as a supplement to the online version of this article at http://www.ajph.org]). We found no differences in social determinants of health, such as poverty or unemployment; however, MSM in New Orleans were more likely to have any insurance, have Medicaid, or have visited a provider in the past 12 months. This suggests that the differences between expansion and nonexpansion states in our analysis are not solely attributable to preexisting geographic inequities.
Descriptive statistics stratified by HIV status are available in Table B (available as a supplement to the online version of this article at http://www.ajph.org). HIV-positive MSM in nonexpansion states were more likely to be employed and less likely to be homeless than were HIV-positive MSM in expansion states. Despite socioeconomic advantages, HIV-positive MSM in nonexpansion states were less likely to be insured (75.1% vs 92.6%), have Medicaid (5.7% vs 37.3%), or have visited a provider in the past 12 months (91.7% vs 95.2%), all factors associated with viral suppression.20
HIV-negative MSM in nonexpansion states were less likely to have insurance (70.4% vs 86.7%), have Medicaid (3.2% vs 17.1%), visited a provider (78.3% vs 85.7%), or come out to their provider (77.4% vs 85.2%). HIV-negative MSM in nonexpansion states were also less likely to have discussed PrEP with a provider (44.3% vs 58.8%) or have used PrEP in the past 12 months (17.8% vs 31.1%) than were MSM in expansion states (Figure 1).
FIGURE 1—
Differences in Preexposure Prophylaxis (PrEP) Discussion and Use Between HIV-Negative Men Who Have Sex With Men (MSM) Living in Medicaid Expansion Versus Nonexpansion States: United States, National HIV Behavioral Surveillance, 2017
Note. CI = confidence interval. MSM live in a state that implemented Medicaid expansion before June 1, 2017
Our first model (Table 3) assessed the relationship between Medicaid expansion and having insurance. MSM in expansion states were more likely to have insurance (APR = 1.14; 95% CI = 1.07, 1.22).
TABLE 3—
Adjusted Association Between Medicaid Expansion Status and Insurance, Medicaid, or PrEP Use Among MSM: National HIV Behavioral Surveillance, 2017
| Model 1: Currently Insured (n = 8729) |
Model 2: Medicaid vs Other Insurancea (n = 7222) |
Model 3: Used PrEP, 12 mo (n = 4919)b |
||||
| CPRc (95% CI) | APR (95% CI) | CPRc (95% CI) | APR (95% CI) | CPRc (95% CI) | APR (95% CI) | |
| Expanded Medicaid (Ref = No) | 1.23 (1.18, 1.28) | 1.15 (1.06, 1.26) | 4.55 (3.40, 6.08) | 5.62 (3.75, 8.41) | 1.78 (1.52, 2.09) | 1.16 (1.01, 1.40) |
| Mo since expansion | 1.00 (1.00, 1.00) | 1.00 (1.00, 1.00) | 0.99 (0.98, 0.99) | 0.99 (0.99, 1.00) | 1.00 (1.00, 1.01) | 1.00 (1.00, 1.00) |
| Regiond (Ref = other): South | 1.03 (0.97, 1.09) | 1.01 (0.96, 1.07) | 1.06 (0.85, 1.31) | 0.96 (0.79,1.16) | 1.00 (0.78, 1.28) | 0.97 (0.80, 1.18) |
| Race/ethnicity (Ref = Non-Hispanic White) | ||||||
| Non-Hispanic Black | 0.96 (0.93, 1.00) | 0.99 (0.96, 1.03) | 2.69 (2.21, 3.27) | 1.53 (1.27, 1.84) | 0.68 (0.57, 0.82) | 0.81 (0.71, 0.93) |
| Hispanic/Latino | 0.89 (0.85, 0.93) | 0.92 (0.88, 0.96) | 1.80 (1.46, 2.22) | 1.33 (1.11, 1.59) | 0.75 (0.64, 0.88) | 0.94 (0.85, 1.05) |
| Other/Multiraciale | 0.96 (0.91, 1.00) | 0.98 (0.93, 1.03) | 1.36 (1.01, 1.82) | 1.10 (0.85, 1.42) | 0.81 (0.66, 1.00) | 0.95 (0.81, 1.11) |
| Age, y (Ref = 50–64) | ||||||
| 18–29 | 0.88 (0.84, 0.91) | 0.91 (0.87, 0.95) | 1.57 (1.25, 1.97) | 1.54 (1.22, 1.95) | 1.50 (1.16, 1.95) | 1.18 (0.96, 1.45) |
| 30–39 | 0.90 (0.86, 0.94) | 0.91 (0.87, 0.95) | 1.16 (0.90, 1.49) | 1.34 (1.05, 1.71) | 1.77 (1.34, 2.32) | 1.30 (1.06, 1.61) |
| 40–49 | 0.95 (0.91, 0.99) | 0.96 (0.91, 1.00) | 0.96 (0.72, 1.28) | 1.02 (0.77, 1.34) | 1.59 (1.19, 2.12) | 1.39 (1.11, 1.73) |
| Employment (Ref = Other/None): Full time | 1.09 (1.05, 1.14) | 1.06 (1.02, 1.11) | 0.41 (0.36, 0.48) | 0.62 (0.53, 0.72) | . . . | . . . |
| Povertyf | ||||||
| ≤ 138% FPL | 1 (Ref) | 1 (Ref) | 4.05 (3.52, 4.67) | 2.84 (2.42, 3.34) | . . . | . . . |
| > 138% FPL | 1.23 (1.18, 1.29) | 1.21 (1.15, 1.27) | 1 (Ref) | 1 (Ref) | . . . | . . . |
| Disability (Ref = No) | . . . | . . . | 1.85 (1.61, 2.13) | 1.34 (1.16, 1.55) | . . . | . . . |
| HIV status (Ref = HIV negative): positive | 1.07 (1.03, 1.10) | 1.08 (1.04, 1.11) | 2.00 (1.74, 2.30) | 1.51 (1.30, 1.76) | . . . | . . . |
| Insurance (Ref = None) | ||||||
| Medicaid only | . . . | . . . | . . . | . . . | 1.87 (1.45, 2.43) | 1.27 (1.05, 1.53) |
| Any other insurance | . . . | . . . | 2.08 (1.68, 2.59) | 1.22 (1.04, 1.44) | ||
| Disclosed sexual identity to provider (Ref = No) | . . . | . . . | . . . | . . . | 3.58 (2.64, 4.86) | 1.12 (0.92, 1.36) |
| Discussed PrEP with provider, 12 mo (Ref = No) | . . . | . . . | . . . | . . . | 17.68 (12.93, 24.17) | 16.62 (12.03, 22.96) |
Note. APR = adjusted prevalence ratio; CI = confidence interval; CPR = crude prevalence ratio; FPL = federal poverty limit; MSM = men who have sex with men; PrEP = preexposure prophylaxis.
Model 2 outcome was limited to MSM with Medicaid vs MSM who reported any other type of health insurance, excluding uninsured MSM.
Model 3 was limited to HIV-negative MSM who were aware of PrEP.
All models accounted for state’s Medicaid expansion status and were weighted for unequal selection probabilities, multiplicity, and nonresponse.
Southern region of residence was defined by the US Census Bureau as living in AL, AR, DE, DC, FL, GA, KY, LA, MD, MS, NC, OK, SC, TN, TX, VA, or WV.
Other racial groups were American Indian/Alaska Native, Asian, Native Hawaiian/Pacific Islander, and multiracial.
Poverty defined by 2017 Department of Health and Human Services federal poverty guidelines: https://www.federalregister.gov/documents/2017/01/31/2017-02076/annual-update-of-the-hhs-poverty-guidelines.
Our second adjusted model assessed the relationship between Medicaid expansion and current Medicaid status. MSM in expansion states were 5.88 times as likely to have Medicaid (95% CI = 4.07, 8.48) as MSM in nonexpansion states.
Our third model assessed the relationship between Medicaid expansion and PrEP use in the past 12 months. MSM living in expansion states were 1.19 times as likely to use PrEP (95% CI = 1.01, 1.40). In expansion states, racial/ethnic PrEP disparities narrowed but persisted. White MSM were more likely than were Black MSM to use PrEP in both expansion (34% vs 26%) and nonexpansion (27% vs 13%; data not in table) states.
DISCUSSION
MSM in states that did not expand Medicaid were less likely to have insurance or utilize health care, including PrEP. Approximately 1 in 5 HIV-positive and 1 in 3 HIV-negative MSM in nonexpansion states were uninsured. MSM in expansion states were more than 5 times as likely to have Medicaid, suggesting that when Medicaid is available, it is used. Because there were no differences in age, poverty, or disability, higher Medicaid use is likely attributable to not higher need but better availability.
We found that PrEP use was lower among HIV-negative MSM in nonexpansion states, although the effect size was small. Most nonexpansion states are in the South, which already experiences higher burden of HIV diagnoses and disproportionately low PrEP uptake.21 Racial/ethnic PrEP disparities could worsen as MSM in Southern nonexpansion states—which have high Black and Hispanic/Latino populations, who bear inequitable HIV burden—continue to have fewer public insurance options.
MSM in expansion states were more likely to use PrEP, consistent with a report showing that PrEP prescriptions among Medicaid recipients increased after New York expanded Medicaid.22 A national study found that a PrEP monthly copay of $20 or more was associated with lower long-term adherence,23 suggesting that no- or low-cost programs are needed for long-term PrEP use. The government program Ready, Set, PrEP provides no-cost PrEP medication to qualified individuals without prescription drug coverage24; however, it does not cover costs of required provider visits or laboratory tests, so some cost barriers may persist.
Black MSM were less likely to use PrEP than were White MSM, regardless of Medicaid expansion. This is consistent with literature showing that Black MSM have low PrEP uptake even when costs are covered.25 Unmeasured factors, including PrEP stigma,26 provider bias,27 and lower access, can help explain this disparity.
MSM in nonexpansion states were less likely to either visit or disclose their sexuality to providers and thus miss the opportunity to talk with a provider about PrEP. Although assistance programs sometimes cover the medication costs, they often do not cover the cost of the 4 CDC-recommended provider visits and laboratory tests each year,8 which involve additional time and financial burden.28 Some MSM may not be able to afford visits or have easy access to clinics. When they do attend, they may not disclose their sexuality, missing the chance for providers to assess risk factors.
Provider attitudes toward PrEP also play a role in uptake. In a study of primary care providers and HIV specialists, most primary care providers were aware of PrEP but rarely discussed or prescribed it.29 Although most HIV specialists were willing to prescribe PrEP, concerns about coverage were the biggest barrier to prescribing it.29 Another study found that the lack of HIV training explained why some Southern primary care providers did not prescribe PrEP.30 Training primary care providers about PrEP, initiating PrEP discussions, and cost-assistance programs is important because HIV-negative men who are not already using PrEP might not see an HIV specialist or feel comfortable initiating the topic with their primary care provider. PrEP expansion efforts may need to address providers’ PrEP attitudes and include fostering stigma-free clinics where patients feel comfortable disclosing their sexuality.
HIV-positive MSM reported differences in insurance coverage and type. About 1 in 13 HIV-positive MSM in expansion and 1 in 4 in nonexpansion states were uninsured. Medicaid is the largest source of coverage for people with HIV, and Medicaid coverage increased substantially for HIV patients after expansion.31 This is consistent with our results showing that HIV-positive MSM in expansion states were 7 times more likely to have Medicaid.
Before the ACA, people with HIV struggled to obtain health insurance because of the preexisting conditions exclusion, cost barriers, and Medicaid eligibility limitations that required disability status.4 Although Medicaid expansion insured more HIV patients, it did not necessarily result in better care quality. In some cases, HIV patients who previously received comprehensive services in a medical home model of care through the Ryan White program suddenly had to navigate a fractured, culturally incompetent system; however, these patients were also newly covered for more non-HIV illnesses under Medicaid.32 Sometimes Ryan White patients receive treatment elsewhere but rely on the Ryan White program to provide support services, such as case management,33 that are often not covered by Medicaid.34 A nationally representative survey of HIV patients found that patients with Medicaid coverage supplemented by the Ryan White program had better viral suppression outcomes than did patients with Medicaid alone.31 However, the Ryan White program is intended as a safety net, so Medicaid expansion may alleviate its burden.
Limitations
This analysis had several limitations. First, NHBS collected data using venue-based sampling in cities with high HIV burden. Men who live in cities and attend MSM-majority venues may have higher incomes, be more likely to be out, and have easier access to clinics and PrEP programs than are men in nonurban areas. Second, not all states are represented in the NHBS sample, and some states had greater representation. Third, behavioral data are self-reported and subject to social desirability and recall biases. Unmeasured factors likely influenced outcomes. Unmeasured factors that could affect insurance status include marital or legal status. PrEP use could be influenced by stigma, PrEP program, trial participation, and clinic factors, such as distance, waitlists, hours, and provider attitudes. Other state policy changes concurrent with expansion likely occurred; therefore, not all differences are attributable to Medicaid expansion. All NHBS nonexpansion states were in the South and were not representative of all nonexpansion states. Regional disparities in access to care existed before the ACA, so differences may not be attributable to Medicaid expansion. Finally, NHBS data are cross-sectional and may not support causal inferences.
Despite these limitations, this analysis highlights differences in care coverage and utilization for both HIV-positive and HIV-negative MSM in diverse US cities.
Conclusions
MSM living in nonexpansion states reported lower health care coverage and utilization, including PrEP use. Lower access and utilization of care could have implications for curbing new HIV infections and present a challenge for making the goal of ending the HIV epidemic a reality.
Public Health Implications
Medicaid can help HIV-positive MSM access the care they need to stay healthy and HIV-negative MSM access life-saving medicines like PrEP. Studies, including this analysis, have shown that health care coverage, access, and outcomes were better35 in expansion states, even when other socioeconomic factors were worse or similar. States may consider expanding Medicaid while carefully considering care quality, coverage of support services, and cultural competency.
ACKNOWLEDGMENTS
Funding for the National HIV Behavioral Surveillance (NHBS) system is provided by the Centers for Disease Control and Prevention (CDC). This research was supported in part by an appointment to the Research Participation Program at the CDC administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and the CDC.
We thank NHBS participants, data management contractors, the CDC Behavioral Surveillance Team, and the NHBS Study Group: Atlanta, GA: Pascale Wortley, Jeff Todd, David Melton; Baltimore, MD: Colin Flynn, Danielle German; Boston, MA: Monina Klevens, Rose Doherty, Conall O’Cleirigh; Chicago, IL: Stephanie Masiello Schuette, David Kern, Antonio D. Jimenez; Dallas, TX: Jonathon Poe, Margaret Vaaler, Jie Deng; Denver, CO: Alia Al-Tayyib, Melanie Mattson; Detroit, MI: Vivian Griffin, Emily Higgins, Mary-Grace Brandt; Houston, TX: Salma Khuwaja, Zaida Lopez, Paige Padgett; Los Angeles, CA: Ekow Kwa Sey, Yingbo Ma; Memphis, TN: Shanell L. McGoy, Meredith Brantley, Randi Rosack; Miami, FL: Emma Spencer, Willie Nixon, David Forrest; Nassau-Suffolk, NY: Bridget Anderson, Ashley Tate, Meaghan Abrego; New Orleans, LA: William T. Robinson, Narquis Barak, Jeremy M. Beckford; New York, NY: Sarah Braunstein, Alexis Rivera, Sidney Carrillo; Newark, NJ: Barbara Bolden, Afework Wogayehu, Henry Godette; Philadelphia, PA: Kathleen A. Brady, Chrysanthus Nnumolu, Jennifer Shinefeld; Portland, OR: Sean Schafer, E. Roberto Orellana, Amisha Bhattari; San Diego, CA: Anna Flynn, Rosalinda Cano; San Francisco, CA: H. Fisher Raymond, Theresa Ick; San Juan, PR: Sandra Miranda De León, Yadira Rolón-Colón; Seattle, WA: Tom Jaenicke, Sara Glick; Virginia Beach, VA: Celestine Buyu, Toyah Reid, Karen Diepstra; Washington, DC: Jenevieve Opoku, Irene Kuo; CDC: Monica Adams, Christine Agnew Brune, Qian An, Alexandra Balaji, Dita Broz, Janet Burnett, Johanna Chapin-Bardales, Melissa Cribbin, YenTyng Chen, Paul Denning, Katherine Doyle, Teresa Finlayson, Senad Handanagic, Brooke Hoots, Wade Ivy, Kathryn Lee, Rashunda Lewis, Lina Nerlander, Evelyn Olansky, Gabriela Paz-Bailey, Taylor Robbins, Catlainn Sionean, Amanda Smith, Anna Teplinskaya, Lindsay Trujillo, Cyprian Wejnert, Akilah Wise, Mingjing Xia.
Note. The findings and conclusions presented in this article are those of the authors and do not necessarily represent the views of the CDC.
Note. A previous version of this article posted to our online First Look section with the following title: “Health Care Coverage and Preexposure Prophylaxis (PrEP) Use Among Men Who Have Sex With Men Living in 22 US Cities With Medicaid Expansion, 2017.”
CONFLICTS OF INTEREST
The authors have no conflicts of interest to declare.
HUMAN PARTICIPANT PROTECTION
National HIV Behavioral Surveillance activities were approved by the US Centers for Disease Control and Prevention and by applicable institutional review boards in each participating city. Informed consent was required for participation.
REFERENCES
- 1.Kaiser Family Foundation. Status of state Medicaid expansion decisions: interactive map. 2019. Available at: https://www.kff.org/medicaid/issue-brief/status-of-state-medicaid-expansion-decisions-interactive-map. Accessed June 13, 2019.
- 2.Skopec L, Long SK. Lesbian, gay, and bisexual adults making gains in health insurance and access to care. Health Aff (Millwood) 2015;34(10):1769–1773. doi: 10.1377/hlthaff.2015.0826. [DOI] [PubMed] [Google Scholar]
- 3.Garfield R, Orgera K, Damico A. The coverage gap: uninsured poor adults in states that do not expand Medicaid. 2019. Available at: https://www.kff.org/medicaid/issue-brief/the-coverage-gap-uninsured-poor-adults-in-states-that-do-not-expand-medicaid. Accessed December 28, 2020.
- 4.Snider JT, Juday T, Romley JA et al. Nearly 60,000 uninsured and low-income people with HIV/AIDS live in states that are not expanding Medicaid. Health Aff (Millwood) 2014;33(3):386–393. doi: 10.1377/hlthaff.2013.1453. [DOI] [PubMed] [Google Scholar]
- 5.Lewis R, Baugher AR, Finlayson T et al. Healthcare access and utilization among persons who inject drugs in Medicaid expansion and nonexpansion states: 22 United States cities, 2018. J Infect Dis. 2020;222(suppl 5):S420–S428. doi: 10.1093/infdis/jiaa337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.HIV.gov. What is ending the HIV epidemic: a plan for America? 2019. Available at: https://www.hiv.gov/federal-response/ending-the-hiv-epidemic/overview. Accessed December 28, 2020.
- 7.Bradley H, Rosenberg ES, Holtgrave DR. Data-driven goals for curbing the US HIV epidemic by 2030. AIDS Behav. 2019;23(3):557–563. doi: 10.1007/s10461-019-02442-7. [DOI] [PubMed] [Google Scholar]
- 8.Centers for Disease Control and Prevention. Pre-exposure prophylaxis (PrEP) Available at: https://www.cdc.gov/hiv/risk/prep/index.html. Accessed June 13, 2019.
- 9.Sullivan PS, Giler RM, Mouhanna F et al. Trends in the use of oral emtricitabine/tenofovir disoproxil fumarate for pre-exposure prophylaxis against HIV infection, United States, 2012–2017. Ann Epidemiol. 2018;28(12):833–840. doi: 10.1016/j.annepidem.2018.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Marcus JL, Hurley LB, Dentoni-Lasofsky D et al. Barriers to preexposure prophylaxis use among individuals with recently acquired HIV infection in Northern California. AIDS Care. 2019;31(5):536–544. doi: 10.1080/09540121.2018.1533238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Simmons-Duffin S. AIDS activists take aim at Gilead to lower price of HIV drug PrEP. 2019. Available at: https://www.npr.org/sections/health-shots/2019/05/30/727731380/old-fight-new-front-aids-activists-want-lower-drug-prices-now. Accessed December 28, 2020. [Google Scholar]
- 12.US Preventive Services Task Force. Final recommendation statement: prevention of human immunodeficiency virus (HIV) infection: preexposure prophylaxis. Available at: https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/prevention-of-human-immunodeficiency-virus-hiv-infection-pre-exposure-prophylaxis. Accessed December 28, 2020.
- 13.Brooks T, Roygardner L, Artiga S, Pham O, Dolan R. Where are states today? Medicaid and CHIP eligibility, enrollment, and cost-sharing policies as of January 2020: findings from a 50-state survey. Available at: https://www.kff.org/medicaid/report/medicaid-and-chip-eligibility-enrollment-and-cost-sharing-policies-as-of-january-2020-findings-from-a-50-state-survey. Accessed December 30, 2020.
- 14.Paz-Bailey G, Raymond HF, Lansky A, Mermin J. Using the National HIV Behavioral Surveillance System to inform HIV prevention efforts in the United States. AIDS Behav. 2014;18(suppl 3):S233–S236. doi: 10.1007/s10461-014-0738-7. [DOI] [PubMed] [Google Scholar]
- 15.Centers for Disease Control and Prevention. HIV infection risk, prevention, and testing behaviors among men who have sex with men. National HIV Behavioral Surveillance: 23 US cities. 2017. Available at: https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-special-report-number-22.pdf, 22. Accessed December 28, 2020.
- 16.Hall C, Rudowitz R, Gifford K. Medicaid in the territories: program features, challenges, and changes. 2019. Available at: https://www.kff.org/report-section/medicaid-in-the-territories-program-features-challenges-and-changes-issue-brief. Accessed June 25, 2019.
- 17.Department of Health and Human Services. Annual update of the HHS poverty guidelines. Fed Regist. 2017;82(19):8831–8832. Available at: https://www.federalregister.gov/documents/2017/01/31/2017-02076/annual-update-of-the-hhs-poverty-guidelines. Accessed December 28, 2020. [Google Scholar]
- 18.National HIV Behavioral Surveillance. NHBS Round 5 CAPI Reference Questionnaire (CRQ) Available at: https://www.cdc.gov/hiv/pdf/statistics/systems/nhbs/NHBS_CRQ_R5_HET5.pdf. Accessed December 28, 2020.
- 19.Mullins TLK, Zimet G, Lally M, Xu J, Thornton S, Kahn JA. HIV care providers’ intentions to prescribe and actual prescription of pre-exposure prophylaxis to at-risk adolescents and adults. AIDS Patient Care STDS. 2017;31(12):504–516. doi: 10.1089/apc.2017.0147. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Beer L, Mattson CL, Bradley H, Skarbinski J. Medical Monitoring Project. Understanding cross-sectional racial, ethnic, and gender disparities in antiretroviral use and viral suppression among HIV patients in the United States. Medicine (Baltimore) 2016;95(13):e3171. doi: 10.1097/MD.0000000000003171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Centers for Disease Control and Prevention. Racial/Ethnic disparities in HIV preexposure prophylaxis among men who have sex with men—23 urban areas, 2017. MMWR Morb Mortal Wkly Rep. 2019;68(37):801–806. doi: 10.15585/mmwr.mm6837a2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Centers for Disease Control and Prevention. Vital signs: increased Medicaid prescriptions for preexposure prophylaxis against HIV infection—New York, 2012–2015. MMWR Morb Mortal Wkly Rep. 2015;64(46):1296–1301. doi: 10.15585/mmwr.mm6446a5. [DOI] [PubMed] [Google Scholar]
- 23.Coy KC, Hazen RJ, Kirkham HS, Delpino A, Siegler AJ. Persistence on HIV preexposure prophylaxis medication over a 2-year period among a national sample of 7148 PrEP users, United States, 2015 to 2017. J Int AIDS Soc. 2019;22(2):e25252. doi: 10.1002/jia2.25252. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.HIV.gov. Ready, set, PrEP. 2019. Available at: https://www.hiv.gov/federal-response/ending-the-hiv-epidemic/prep-program. Accessed December 28, 2020.
- 25.Rolle CP, Rosenberg ES, Siegler AJ et al. Challenges in translating PrEP interest into uptake in an observational study of young Black MSM. J Acquir Immune Defic Syndr. 2017;76(3):250–258. doi: 10.1097/QAI.0000000000001497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cahill S, Taylor SW, Elsesser SA, Mena L, Hickson D, Mayer KH. Stigma, medical mistrust, and perceived racism may affect PrEP awareness and uptake in Black compared to White gay and bisexual men in Jackson, Mississippi and Boston, Massachusetts. AIDS Care. 2017;29(11):1351–1358. doi: 10.1080/09540121.2017.1300633. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Calabrese SK, Earnshaw VA, Underhill K, Hansen NB, Dovidio JF. The impact of patient race on clinical decisions related to prescribing HIV pre-exposure prophylaxis (PrEP): assumptions about sexual risk compensation and implications for access. AIDS Behav. 2014;18(2):226–240. doi: 10.1007/s10461-013-0675-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sullivan PS, Siegler AJ. Getting pre-exposure prophylaxis (PrEP) to the people: opportunities, challenges and emerging models of PrEP implementation. Sex Health. 2018;15(6):522–527. doi: 10.1071/SH18103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Petroll AE, Walsh JL, Owczarzak JL, McAuliffe TL, Bogart LM, Kelly JA. PrEP awareness, familiarity, comfort, and prescribing experience among US primary care providers and HIV specialists. AIDS Behav. 2017;21(5):1256–1267. doi: 10.1007/s10461-016-1625-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Henny KD, Duke CC, Geter A et al. HIV-related training and correlates of knowledge, HIV screening and prescribing of nPEP and PrEP among primary care providers in Southeast United States, 2017. AIDS Behav. 2019;23(11):2926–2935. doi: 10.1007/s10461-019-02545-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Dawson L, Kates J. Insurance coverage changes for people with HIV under the ACA. Available at: https://www.kff.org/report-section/an-update-on-insurance-coverage-among-people-with-hiv-in-the-united-states-findings. Accessed December 28, 2020.
- 32.Arnold EA, Fuller S, Kirby V, Steward WT. The impact of Medicaid expansion on people living with HIV and seeking behavioral health services. Health Aff (Millwood) 2018;37(9):1450–1456. doi: 10.1377/hlthaff.2018.0414. [DOI] [PubMed] [Google Scholar]
- 33.Kaiser Family Foundation. The Ryan White HIV/AIDS Program: the basics. 2019. Available at: https://www.kff.org/hivaids/fact-sheet/the-ryan-white-hivaids-program-the-basics. Accessed December 28, 2020.
- 34.Dawson L, Kates J, Perry M, Perry K. The ACA and people with HIV: an update. 2016. Available at: https://www.kff.org/health-reform/issue-brief/the-aca-and-people-with-hiv-an-update. Accessed August 13, 2020.
- 35.Chen J, Vargas-Bustamante A, Mortensen K, Ortega AN. Racial and ethnic disparities in health care access and utilization under the Affordable Care Act. Med Care. 2016;54(2):140–146. doi: 10.1097/MLR.0000000000000467. [DOI] [PMC free article] [PubMed] [Google Scholar]