The Health Equity Implications of the HRSA Ryan White HIV/AIDS Program

SUMMARY

Objective:

Investigate the role of the Ryan White HIV/AIDS Program (RWHAP)—which funds services for vulnerable and historically disadvantaged populations with HIV—in reducing health inequities among people with HIV over a 10-year horizon.

Design:

We use an agent-based microsimulation model to incorporate the complexity of the program and long-time horizon.

Methods:

We use a composite measure (the Theil index) to evaluate the health equity implications of the RWHAP for each of four subgroups (based on race and ethnicity, age, gender, and HIV transmission category) and two outcomes (probability of being in care and treatment and probability of being virally suppressed). We compare results with the RWHAP fully funded versus a counterfactual scenario, in which the medical and support services funded by the RWHAP are not available.

Results:

The model indicates the RWHAP will improve health equity across all demographic subgroups and outcomes over a 10-year horizon. In Year 10, the Theil index for race and ethnicity is 99% lower for both outcomes under the RWHAP compared to the non-RWHAP scenario; 71 to 93% lower across HIV transmission categories; 31 to 44% lower for age; and 73 to 75% lower for gender.

Conclusion:

Given the large number of people served by the RWHAP and our findings on its impact on equity, the RWHAP represents an important vehicle for achieving the health equity goals of the National HIV/AIDS Strategy (2022–2025) and the Ending the HIV Epidemic Initiative goal of reducing new infections by 90% by 2030.

INTRODUCTION

Inequities in HIV prevention and treatment are prevalent in the United States. Blacks, Hispanics, and transgender communities face structural barriers to care and treatment that result in higher rates of HIV incidence, prevalence, and mortality among these minority groups compared to non-Hispanic Whites and cisgender individuals.^1–3 For example, 70% of new HIV infections in the United States are among Black and Hispanic populations,⁴ and 4 in 10 transgender women surveyed in seven U.S. cities were HIV positive.⁵ Studies also show consistently lower rates of retention in care, use of antiretroviral treatment (ART), viral suppression, and use of pre-exposure prophylaxis among racial and ethnic minority populations compared to non-Hispanic Whites.^6–8 Poorer health outcomes among racial and ethnic minority groups stem from a wide range of factors, including higher rates of uninsurance, limited access to health care services, and other structural barriers to care, such as housing, transportation, employment, and poverty, as well as stigma and mistrust of the medical profession.^6,7,9–11

To address these challenges, the White House Office of Infectious Disease and HIV/AIDS Policy included as one of the goals of the National HIV/AIDS Strategy for the United States 2022–2025 (NHAS) the reduction of HIV-related disparities and health inequities among racial, ethnic, sexual, and gender minority groups.¹² Recent research indicates that addressing structural inequities in health care is critical to achieving broad improvements in HIV-related health outcomes and ending the HIV epidemic in this country.¹³ According to the NHAS, this includes reducing HIV-related stigma and discrimination; reducing disparities in new HIV infections, in knowledge of HIV status, and along the HIV care continuum; addressing social and structural determinants of health; and training and expanding a diverse HIV workforce.¹²

The Ryan White HIV/AIDS Program (RWHAP), administered by the Health Resources and Services Administration (HRSA) in the U.S. Department of Health and Human Services, can play an important role in achieving these goals.¹⁴ The RWHAP provides a comprehensive system of HIV primary medical care, essential support services, and medication for people with HIV who have low incomes and are uninsured or underserved. More than half of people diagnosed with HIV in the United States receive services through the RWHAP each year and the federal program reduces barriers to care that might otherwise impede improvements in HIV health outcomes.^15,16 The RWHAP represents a substantial investment of public resources—representing the third largest source of federal funding for HIV care and treatment in the United States after Medicare and Medicaid. Research examining the long-term public health and economic impact of the RWHAP using an agent-based model (ABM) found that the RWHAP is cost-effective.¹⁷

Understanding the distributional of impacts of the RWHAP is critically important to ending the HIV epidemic. In this paper, we investigate the impact of the program on health equity among people with HIV. We initialize the model using 2016 data and project health equity over a 10-year horizon (2016–2026) using an ABM. We estimate this impact by comparing the scenario in which the RWHAP is implemented as currently funded relative to a counterfactual scenario representing the absence of the RWHAP, defined by the lack of availability of the services the program funds. ABMs serve as a useful tool to investigate the counterfactual scenario and equity as such an experiment would be infeasible.¹⁸ We employ a composite measure of health equity (Theil index) based on the weighted average of the difference between each subgroup’s outcome and the overall population mean. We examine the health equity implications of the program over four priority populations based on race and ethnicity, age, gender, and HIV transmission category on two outcomes (probability of being in care and treatment and probability of being virally suppressed).

METHODS

Model description

Our analysis builds on an ABM developed to estimate the cost-effectiveness of the RWHAP.^17,19 The model is parameterized to represent people with HIV, as well as those at risk of acquiring HIV, in the United States. Agents in the model progress through seven potential care stages: (1) HIV negative, (2) HIV positive but status unknown, (3) diagnosed and status known but not receiving care, (4) in care and treatment but not virally suppressed, (5) virally suppressed, (6) left care and treatment, and (7) death. Each month, an individual either remains in the current stage or transitions to another stage. The model simulates interactions among agents through male-to-male and heterosexual sexual contact as well as the sharing of needles among people who use injection drugs; transmission of HIV can occur through these interactions, which transitions a person from Stage 1 to Stage 2. The services a person with HIV needs and receives affect the rate at which a person transitions among Stages 3–7. See Figure 1 for a schematic of the model.

Figure 1. Disease model structure for the model population including various stages of care.

Purple arrows indicate transitions affected by receipt of needed ancillary services, including MCM, mental health and substance abuse services, and other non-OAHS medical care and support services. People can transition from any care stage (Stages 2–6) to death (Stage 7).

MCM = medical case management; OAHS = outpatient ambulatory health service.

Data

To estimate the demographic and risk group characteristics of people served by the RWHAP and those with HIV who receive care outside the RWHAP within each care stage, we use individual-level data from the 2016 RWHAP Services Report (RSR), 2015 AIDS Drug Assistance Program Data Report (ADR), and Centers for Disease Control and Prevention (CDC) National HIV Surveillance System data. We use evidence from the literature to identify the transition probabilities between the care stages for each subgroup, as well as to determine the health states associated with each care stage. See Goyal et al. (2021) for additional information on the model and parameters and see the Supplement for parameter updates.¹⁷

Study population

The model includes all people with diagnosed and undiagnosed HIV in the United States. The model characterizes each person by age group (ages 13–24, 25–54, and 55 or older); gender (male and female); race and ethnicity (Black, Hispanic, and White/all others); and HIV transmission category (male-to-male sexual contract only, IDU only, male-to-male sexual contact and IDU, and heterosexual contact). Transgender people are not explicitly modeled due to the limited data on this population. Estimation for transmission category parameters is based on the RSR, which represents transmission attributable to exposure and might not reflect current behavior of an individual. Many of the model parameters (such as probability of acquiring HIV, probability of being diagnosed, and probability of being retained in care and virally suppressed) vary across the subgroups within one or more of the four demographic characteristics.¹⁹ The definitions of the categories within each demographic characteristic ensure that each subgroup has enough individuals to derive interpretable results.

RWHAP services

The HIV care and treatment services funded under the RWHAP are organized into five mutually exclusive categories:²⁰ (1) outpatient ambulatory health services (OAHS), (2) ART, (3) medical case management, (4) mental health and substance abuse (MH/SA) services, and (5) all other core and support services. The first category, OAHS, is the RWHAP term for diagnostic or therapeutic services provided by a licensed health care provider in an outpatient medical setting. The last category includes all other RWHAP-funded non-OAHS medical and support services, such as home-based and community-based health services, non-medical case management, health education and risk reduction counseling, medical transportation, legal assistance, and housing services, among others.

The RSR and ADR enabled us to calculate the percentages of RWHAP individuals who received each of these services, whereas we used CDC reports to determine the services received by people with HIV who do not use RWHAP services.¹⁹ These percentages are stratified by demographic and risk group characteristics. We assume all people with HIV need OAHS and ART. However, the need for medical case management, MH/SA services, and all other core and support services varies by demographic characteristics (age, gender, race and ethnicity, and HIV transmission category). We also assumed service need is the same (conditional on demographic and transmission group) for people with HIV who received care under the RWHAP and those who did not. The probability of receiving a needed service also depends on whether a person is in the RWHAP. To estimate the need for services, we summed receipt and unmet need. To measure unmet need for those who are in care, Weiser et al.²¹ used data from the CDC’s Medical Monitoring Project to estimate unmet need for patients with HIV infection who accessed care at RWHAP-funded facilities versus those who accessed care at facilities not funded by the RWHAP.

A person’s transition through the care stages depends largely on whether service needs are met. For example, a person who needs and receives medical case management has a higher probability of remaining in care and treatment and becoming virally suppressed than a person who needs but does not receive the service.

Validation

Our approach consists of four forms of validation based on established recommendations for mathematical models: face validity, verification or internal validity, cross validity, and external validity.²² To validate the model, we assume that the HIV epidemic is at a steady state. We base this steady state using 2016 data—the most recently available data at the level of disaggregation needed for this analysis at the time of conducting the study. Because the HIV epidemic has become more concentrated among vulnerable populations,²³ our findings on the impact of the RHWAP should be considered conservative. See the Supplement for additional details on validation. We define a good-fitting result as one in which model-projected results are within 15 percent of existing benchmarks.²⁴

Model outcomes

We simulate two scenarios: (1) a status quo scenario with no change in the current funding and composition of the RWHAP; and (2) a counterfactual scenario, in which the medical and support services funded by the RWHAP are not available. We simulate identical populations for the two scenarios. The only difference between the scenarios is the extent of unmet need of services, which is stratified by demographic characteristics and services. We make a conservative assumption that, in the absence of the RWHAP, only uninsured people lose access to the medical and support services they need. We calculate the proportion of uninsured RWHAP individuals by demographic and risk characteristics based on the RSR and ADR.

e compare results between the two scenarios to calculate the impact of the RWHAP on the percentage in care and treatment and the percentage virally suppressed, measured over all people with HIV, regardless of their HIV diagnosis. We calculate these outcomes for the entire population and separately for each set of demographic characteristics (for example, across the three racial and ethnic categories). We use a 10-year time horizon in this analysis. Based on U.S. Public Health Service Task Force guidelines, we discount future health outcomes at an annual rate of 3% to reflect the lower value of a delayed benefit or expense.²⁵

Health equity analysis

RWHAP funding addresses health disparities by expanding access to HIV treatment and providing nonmedical services to mitigate the effect of poverty and the lack of transportation, housing, and other social needs on health outcomes. The model partially captures the effect of these services through the impact of RWHAP-funded support, and MH/SA services on adherence and retention. Specifically, in our model, four mechanisms drive inequities in HIV outcomes across demographic populations. The first factor—potentially addressed by the RWHAP—is differential use of needed services by demographic characteristics. The model assumes that barriers to accessing HIV treatment and services exacerbate health disparities.²⁶ The second is a result of a combination of complex issues, such as structural and social barriers, associated with access to testing facilities and stigma of testing.²⁷ We capture this factor by having varying time from infection to diagnosis based on gender and HIV transmission category. The third factor is limited access to harm reduction initiatives, which increases the risk of acquiring and transmitting HIV among those who use injection drugs.²⁸ This increase in risk is captured by having people engaged in injection drug use susceptible to transmission from both sexual contact and IDU. The fourth factor in the model is biological viral transmission risk due to type of sexual intercourse.²⁹ Having an increased transmission risk for a male-to-male sexual contact compared to a heterosexual sexual contact captures this factor.

We use the Theil index to measure health equity across the subgroups.³⁰ The Theil index provides a relative, weighted measure of the discrepancies between each subgroup’s outcome (for example, viral suppression among Blacks versus Whites) and the overall population mean; see the Theil Section of the Supplement for additional details on the index. We base our conclusions on a comparison of the Theil index under RWHAP versus non-RWHAP scenarios at Year 10 of the simulation. For each of our simulation model outcomes, we provide a point estimate as well as a 95% prediction interval over all simulations corresponding to each scenario; see the Estimation Section of the Supplement for additional details on these calculations and sources of simulation stochasticity.

FINDINGS

Characteristics of people living with HIV at model initialization

The model initializes the approximately 1.1 million people ages 13 years or older who are living with diagnosed or undiagnosed HIV infection in the United States. Of these, most are 55 years or older (69.3%), male (77.2%), and at risk of acquiring HIV through male-to-male sexual contact (56.3%). The population of people living with HIV was more evenly distributed by race and ethnicity: 43.4% were Black, 23.7% are non-White Hispanic, and 32.9% were White or other. The model designates 15% of people living with HIV as undiagnosed, and another almost 20% are either diagnosed but had never been in care or had been in care and treatment at some point in their lives but had subsequently left. The remaining two-thirds of people with HIV are either in care and treatment but not virally suppressed (11.6%) or in care and treatment and virally suppressed (55%). See Table 1 for additional details.

Table 1.

Demographic characteristics and care stage of people with HIV in the United States in 2016

Characteristic	Category	Number	Percent
Total		1,100,000	100%
Age group	13–24 years	48,344	4.4%
	25–54 years	761,822	69.3%
	More than 54 years	289,834	26.3%
Gender	Male	848,921	77.2%
	Female	251,079	22.8%
Risk group	MSM only	619,761	56.3%
	IDU only	133,925	12.2%
	MSM and IDU	56,343	5.1%
	Heterosexual contact	289,971	26.4%
Race and ethnicity	Black	476,969	43.4%
	Hispanic	260,874	23.7%
	White and all others	362,157	32.9%
Model stage	Description	Number	Percent
Total		1,100,000	100%
Stage 2	HIV positive but undiagnosed	159,204	14.5%
Stage 3	Diagnosed and status known but not receiving care	48,863	4.4%
Stage 4	In care and treatment but not virally suppressed	127,984	11.6%
Stage 5	Virally suppressed	604,975	55.0%
Stage 6	Left care and treatment	158,974	14.5%

Source: HIV surveillance data, Centers for Disease Control and Prevention, 2016 Ryan White HIV/AIDS Program Service Reports, 2016; and AIDS Drug Assistance Program Data Report, 2015.

Notes: Estimates include people living with HIV who have not been diagnosed and exclude those age 12 and younger. Stages 1 and 7 (not shown) are HIV negative but at risk for acquiring HIV infection and death, respectively.

IDU = injection drug use; MSM = men who have sex with men; RWHAP = Ryan White HIV/AIDS Program.

Sources: HIV surveillance data, Centers for Disease Control and Prevention, 2016 Ryan White HIV/AIDS Program Service Reports, 2016; and AIDS Drug Assistance Program Data Report, 2015.

Note: Estimates include people living with HIV who have not been diagnosed and exclude those ages 12 and younger. Stages 1 and 7 (not shown) are HIV negative but at risk for acquiring HIV infection and death, respectively.

IDU = injection drug use; MSM = men who have sex with men; RWHAP = Ryan White HIV/AIDS Program.

Table 2 provides a summary of unmet needs while a person is in care, by demographic characteristic and service category for the two scenarios; the model assumes all needs are unmet while a person is not in care. Table 2 reveals two findings important for our model. First, overall unmet need is lower under the RWHAP scenario compared to the counterfactual scenario without the RWHAP. Second, unmet need is fairly constant across subgroups with the RWHAP but varies by demographic and transmission category in the absence of the program.

Table 2.

Percentage of people with HIV with unmet needs, by demographic characteristic, service, and scenario

Service		OAHS and ART		MCM		MH/SA		Other medical and support
Characteristic	Category Scenario	With RWHAP	Without RWHAP	With RWHAP	Without RWHAP	With RWHAP	Without RWHAP	With RWHAP	Without RWHAP
Total		0%	28.1%	8.5%	40.9%	17.7%	45.2%	21.9%	50.0%
Age group	13–24 years	0%	24.9%	7.9%	42.0%	16.6%	45.1%	21.6%	51.3%
	25–54 years	0%	28.3%	8.5%	42.1%	17.8%	46.2%	21.9%	51.2%
	More than 54 years	0%	28.0%	8.5%	37.3%	17.7%	42.6%	21.9%	46.9%
Gender	Male	0%	28.9%	8.6%	42.0%	17.8%	46.3%	21.9%	50.9%
	Female	0%	25.2%	8.1%	37.6%	17.3%	42.2%	21.8%	47.4%
Risk group	MSM only	0%	30.7%	8.4%	44.3%	17.7%	48.7%	21.8%	52.9%
	IDU only	0%	25.3%	8.5%	35.0%	17.6%	41.1%	21.9%	44.8%
	MSM and IDU	0%	16.1%	10.1%	27.7%	19.1%	34.6%	22.4%	37.1%
	Heterosexual contact	0%	26.0%	8.3%	39.5%	17.5%	43.7%	21.8%	49.0%
Race and ethnicity	Black	0%	25.7%	8.4%	39.4%	17.7%	43.7%	21.9%	48.8%
	Hispanic	0%	30.3%	8.5%	45.1%	17.7%	47.8%	21.9%	53.8%
	White and all others	0%	29.6%	8.5%	39.9%	17.7%	45.1%	21.9%	49.0%

Sources: Ryan White HIV/AIDS Program Service Reports, 2016; and Weiser et al. 2015.

Note: Estimates of unmet need are calculated over those who needed the service only. The model assumes everyone needs OAHS and ART and, under the RWHAP, receives these services. Other medical care services include home-based and community-based health services, non-MCM, health education and risk reduction counseling, medical transportation, legal assistance, and housing services, among others.

ART = antiretroviral therapies; IDU = injection drug use; MCM = medical case management; MH/SA = mental health/substance abuse; MSM = men who have sex with men; OAHS = outpatient ambulatory health service; RWHAP = Ryan White HIV/AIDS Program.

Validation

For our validation results, we focus on external validity; see the Supplement for results for the other validation types and additional details. We see strong agreement between our model and observed values for the proportions of new diagnoses by race for each of Years 1, 5, and 10;³¹ all proportions are within our 15% benchmark target (Supplement Table 5). Our proportions by age are within our 15% benchmark target for all categories except 55 years and older, which is the smallest age category (Supplement Table 6). For new diagnoses by gender, our model is low for the proportion of female diagnoses (Supplement Table 7). This discrepancy might be the result of increased risk female sex workers have for acquiring HIV,³² which the model does not capture; we mention this as a limitation in the Discussion. If we account for the lower number of females, the model proportions by transmission risk are within our 15% benchmark target for all categories except individuals who are both men who have sex with men (MSM) and IDU in Years 1 and 5—there are a small number of infections in this category—and IDU in Year 1 (Supplement Tables 8 and 9). We show HIV mortality rates match estimates³³ for two populations: all people living with a diagnosed infection of HIV regardless of their ART status and people living with a diagnosed infection of HIV who are on ART (Supplement Table 10). Overall, the model generated valid outputs across a wide range of outputs.

Impact of the RWHAP on care and health outcomes

In Year 10 of the simulation, the model predicts that under the status quo scenario the percentage of people with HIV in care and on treatment will be 65% and the percentage of those who are virally suppressed will be 59% (Table 3). These values align with current national estimates³⁴ after factoring in the estimated number of people with HIV who are undiagnosed and diagnosed but not in care.³⁵ Comparing scenarios, the RWHAP will increase the overall number of people with HIV receiving care and treatment by 44% (to 65% with the RWHAP from 45% without the RWHAP) and number of people with HIV who are virally suppressed by 51% (to 59% with the RWHAP from 39% without the RWHAP), relative to the outcomes that would have occurred in the absence of the program. Reflecting the impact of the RWHAP services on unmet needs by subgroup, the model estimates the RWHAP will have the greatest beneficial impact on outcomes in Year 10 among the adult (ages 25–54), male, MSM, and Hispanic subgroups, though the model indicates all four population subgroups included in this study benefit from the RWHAP. See Table 3 for additional details.

Table 3.

Percentage of people with HIV in care and treatment and virally suppressed in Year 10 with versus without the RWHAP

Characteristic	Category	In Care and Treatment			Virally Suppressed
		With RWHAP	Without RWHAP	Percent Difference	With RWHAP	Without RWHAP	Percent Difference
Total		64.7 (64.6 – 64.7)	45.0 (44.8 – 45.1)	43.7 (43.3 – 44.2)	59.0 (58.9 – 59.1)	39.0 (38.9 – 39.1)	51.2 (50.7 – 51.8)
Age group	13–24 years	45.1 (44.3 – 45.6)	32.2 (31.8 – 32.5)	40.1 (37.8 – 42.6)	38.4 (37.7 – 38.8)	24.9 (24.6 – 25.2)	54.3 (51.2 – 57.3)
	25–54 years	63.7 (63.6 – 63.8)	43.7 (43.5 – 43.8)	45.9 (45.4 – 46.4)	57.9 (57.8 – 58.0)	37.5 (37.3 – 37.6)	54.6 (53.9 – 55.2)
	More than 54 years	68.0 (67.8 – 68.1)	48.9 (48.6 – 49.0)	39.3 (38.7 – 39.9)	62.6 (62.4 – 62.7)	43.3 (43.1 – 43.4)	44.5 (43.9 – 45.3)
Gender	Male	63.7 (63.5 – 63.8)	43.7 (43.5 – 43.8)	45.7 (45.2 – 46.2)	57.9 (57.8 – 58.0)	37.7 (37.5 –37.8)	53.7 (53.2 – 54.3)
	Female	69.1 (69.0 – 69.3)	51.3 (51.2 – 51.5)	34.7 (34.1 – 35.2)	63.6 (63.4 – 63.7)	45.3 (45.2 – 45.5)	40.1 (39.3 – 40.8)
Risk group	MSM only	63.7 (63.5 – 63.8)	42.8 (42.7 – 43.0)	48.7 (48.2 – 49.3)	58.0 (57.8 – 58.0)	36.7 (36.6 – 36.8)	57.8 (57.1 – 58.5)
	IDU only	66.5 (66.2 – 66.7)	49.4 (49.1 – 49.6)	34.6 (33.8 – 35.5)	60.8 (60.5 – 61.0)	44.0 (43.8 – 44.3)	38.0 (37.2 – 38.8)
	MSM and IDU	66.7 (66.4 – 67.0)	53.7 (53.3 – 54.1)	24.3 (22.9 – 25.3)	60.4 (60.2 – 60.7)	47.5 (47.2 – 47.9)	27.2 (25.7 – 28.1)
	Heterosexual contact	66.0 (65.8 – 66.2)	47.4 (47.2 – 47.6)	39.2 (38.5 – 40.0)	60.6 (60.3 – 60.7)	41.4 (41.3 – 41.7)	46.2 (45.3 – 46.9)
Race and ethnicity	Black	64.6 (64.5 – 64.7)	46.0 (45.9 – 46.1)	40.4 (39.9 – 40.9)	58.9 (58.8 – 59.0)	39.7 (39.6 – 39.8)	48.3 (47.7 – 48.9)
	Hispanic	64.4 (64.3 – 64.6)	42.9 (42.6 – 43.0)	50.2 (49.6 – 51.0)	58.8 (58.6 – 59.0)	36.6 (36.3 – 36.7)	60.7 (59.9 – 61.7)
	White and all others	64.9 (64.7 – 65.1)	45.2 (45.0 – 45.4)	43.5 (42.8 – 44.2)	59.3 (59.2 – 59.4)	39.9 (39.7 – 40.1)	48.5 (47.8 – 49.3)

Sources: Ryan White HIV/AIDS Program Service Reports, 2016; AIDS Drug Assistance Program Data Report, 2015; and Centers for Disease Control and Prevention HIV surveillance data, 2016.

Note: Counts exclude people with HIV ages 12 and younger.

IDU = injection drug use; MSM = men who have sex with men; RWHAP = Ryan White HIV/AIDS Program.

Impact of the RWHAP on health equity

The model indicates the RWHAP will have a favorable effect on health equity for both outcomes and all subgroups over a 10-year horizon compared to the counterfactual; see Figure 2. The model predicts the RWHAP will improve health equity the most across racial and ethnic subgroups. In Year 10, the Theil index based on race and ethnicity is 99% lower for both outcomes under the RWHAP compared to the non-RWHAP scenario: 0.0004 versus 0.0395 for rates of care and treatment, and 0.0008 versus 0.0644 for rates of viral suppression. These results are due to the beneficial impact the RWHAP has on Hispanics as seen in Table 3. The model produces favorable results across HIV transmission categories as well, with a Theil index ranging from 71 to 93% lower under the RWHAP compared to the non-RWHAP scenario. For equity based on age, the RWHAP has smaller, though still favorable effects, ranging from 44 to 31% lower compared with the counterfactual. In terms of equity across gender groups, the RWHAP has favorable effects on rates of being in care and treatment (the Theil index is 73% lower under the RWHAP scenario) and virally suppressed (the Theil index is 74% lower under the RWHAP scenario).

Figure 2. Theil index for percentage of people with HIV (a) in care and treatment and (b) virally suppressed at Year 10: With versus without the RWHAP.

The bars represent the medians across all simulations and the brackets around the medians represent the 95% prediction intervals. An asterisk (*) indicates the 95% prediction intervals of the Theil index associated with the RWHAP and non-RWHAP scenarios do not overlap for the demographic characteristic.

DISCUSSION

This study suggests the RWHAP substantially improves health equity based on age, gender, race and ethnicity, and HIV transmission category. These results build upon findings from other studies demonstrating the RWHAP is cost effective and reduces disparities in HIV outcomes by addressing unmet need for medical and support services among people with HIV.^{14,17,19,36,37} Several features of the RWHAP help reduce disparities and promote health equity: (1) serving large proportions of people with low incomes who lack alternative sources of health care coverage;¹⁶ (2) providing a comprehensive and multidisciplinary approach to care that uses a whole person perspective to address complex medical and social needs of people with HIV;³⁷ (3) funding education and capacity-building programs that help providers address issues of stigma, discrimination, and systemic racism; and (4) including people with lived experiences in planning body leadership positions and in service delivery as clinicians, community health workers, and peer navigators.

Two previous publications discuss the model’s limitations.^17,19 An important limitation specific to this study is the absence of transgender people and female sex workers in the model due to insufficient information on services needed and received for this population, which determine how individuals move through the stages. This, compounded by the comparatively small number of people in the HIV population, would produce model results of questionable validity and precision for this population. It is well documented that transgender people and sex workers face many challenges—such as racism, stigma, and transphobic discrimination—that negatively affect care access and HIV-related health outcomes.^32,38

An area of potential research is investigating inequities along other dimensions (beyond demographics and transmission risk) that are of interest, such as geographic location. In addition, there might be intersectional inequities along our demographic and transmission factors that warrant further investigation, such as race by transmission group. Another promising area for future research is developing mathematical models that explicitly model social barriers (such as lack of transportation) and the impacts of interventions addressing those barriers, which will require granular evidence on the effect of these types of assistance on retention in care and achievement of viral suppression.

Given the large number of people served by the RWHAP and its impact on efficiency and equity, the program represents an important vehicle for achieving the improvement in equity goals of the NHAS as well as the reduction in new infection goals of the Ending the HIV Epidemic initiative.³⁹

DATA SHARING

All model parameter values are provided in the text or supplement, or a reference is cited. The model code is available on GitHub: https://github.com/mathematica-pub/abm_hiv/tree/rwhap_equity.