Abstract
Background
In the USA, men who have sex men (MSM) are at high risk for HIV, and black MSM have a substantially higher prevalence of infection than white MSM. We created a simulation model to assess the strength of existing hypotheses and data that account for these disparities.Methods
We built a dynamic, stochastic, agent-based network model of black and white MSM aged 18-39 years in Atlanta, GA, USA, that incorporated race-specific individual and dyadic-level prevention and risk behaviours, network attributes, and care patterns. We estimated parameters from two Atlanta-based studies in this population (n=1117), supplemented by other published work. We modelled the ability for racial assortativity to generate or sustain disparities in the prevalence of HIV infection, alone or in conjunction with scenarios of observed racial patterns in behavioural, care, and susceptibility parameters.Findings
Race-assortative mixing alone could not sustain a pre-existing disparity in prevalence of HIV between black and white MSM. Differences in care cascade, stigma-related behaviours, and CCR5 genotype each contributed substantially to the disparity (explaining 10�0%, 12�7%, and 19�1% of the disparity, respectively), but nearly half (44�5%) could not be explained by the factors investigated. A scenario assessing race-specific reporting differences in risk behaviour was the only one to yield a prevalence in black MSM (44�1%) similar to that observed (43�4%).Interpretation
Racial assortativity is an inadequate explanation for observed disparities. Work to close the gap in the care cascade by race is imperative, as are efforts to increase serodiscussion and strengthen relationships among black MSM particularly. Further work is urgently needed to identify other sources of, and pathways for, this disparity, to integrate concomitant epidemics into models, and to understand reasons for racial differences in behavioural reporting.Funding
The Eunice Kennedy Shriver National Institute of Child Health and Development, the National Institute of Allergy and Infectious Diseases, the National Institute of Minority Health and Health Disparities, and the National Institute of Mental Health.Free full text
Isolating the sources of racial disparities in HIV prevalence among men who have sex with men (MSM) in Atlanta, GA: A modeling study
Associated Data
Abstract
Background
In the United States, men who have sex men (MSM) are a most-affected risk group, with Black MSM (BMSM) experiencing markedly higher HIV prevalence than White MSM (WMSM). We created a simulation model to jointly evaluate the strength of existing hypotheses and data in explaining these disparities.
Methods
A dynamic, stochastic agent-based network model of BMSM and WMSM ages 18-39 in Atlanta incorporated race-specific individual and dyadic-level prevention and risk behaviors, network attributes, and care patterns. We estimated parameters from two Atlanta-based studies of this population (n=1,117), supplemented by the literature. We modeled the ability for racial assortativity to generate or sustain HIV prevalence disparities, alone as well as in conjunction with scenarios of observed racial patterns in behavioral, care, and susceptibility parameters.
Findings
Race-assortative mixing alone could not sustain a pre-existing disparity. Differences in care cascade, stigma-related behaviors, and CCR5 genotypes each contributed considerably to the disparity, but nearly half (45%) of the disparity remained unexplained. A scenario evaluating race-specific reporting differences was the only one to yield Black HIV prevalence similar to that observed.
Interpretation
Racial assortativity is an inadequate explanation for observed disparities. Work to close the gap in the care cascade by race is imperative, as are efforts to increase serodiscussion and strengthen relationships among Black MSM especially. Further work is urgently needed to identify other sources and pathways of this disparity, integrate concomitant STI epidemics into models, and understand reasons for racial differences in behavioral reporting.
Funding
NIH R21HD075662;RC1MD004370;R01MH085600;R01HD067111;R01AI083060; R01HD068395;P30AI027757;P30AI050409;R24HD042828.
Introduction
Men who have sex with men (MSM) account for most new HIV diagnoses in the United States.1 Concentrations of HIV infections among MSM are highest in the US South, where Georgia is uniquely high in both HIV prevalence and cases.2 The MSM HIV epidemic is characterized by marked, longstanding Black/White racial disparities in HIV prevalence and incidence; a recent Atlanta study estimated prevalence of 43% among Black MSM (BMSM) and 13% among White MSM (WMSM), a 3 3-fold difference.3,4 Numerous explanations for the disproportionate impact among BMSM have been offered, and thoroughly reviewed.5,6 These include distal structural factors such as poverty, stigma, and institutionalized racism; however, the proximate causal pathways through which these factors enact disparate epidemics have proven challenging to elucidate. For structural factors to cause differentials in HIV incidence, they must mediate one or both more proximate causes: (1) the frequency with which HIV-negative individuals are potentially exposed to HIV and (2) the probability of transmission given exposure. The latter comprises factors associated with either the HIV-negative partner (e.g., circumcision status) or HIV-positive partner (e.g., viral suppression). Much literature around MSM race disparities focuses on self-reported individual risk behaviors (e.g., number of sex partners, substance use), but their explanatory power is limited since most behaviors are not higher among BMSM.5,6 Inadequate HIV suppression among partners of HIV-negative MSM places them at increased risk of acquisition, with racial disparities in the care continuum likely contributing to racially-disparate HIV incidence.7 Few studies have examined susceptibility differences, save for the protective CCR532 mutation, which is more prevalent among WMSM.8 Differences in sexual network properties are potential explanatory higher-order risk behaviors, although the primary evidence for meaningful differences by race are mixed.4,6,9,10
Gay- and HIV-related stigma may also affect the health of BMSM, influencing their apparent and real HIV risks by shortening partnership durations and suppressing pre-sexual HIV status discussion (serodiscussion). Stigma and mistrust of research may cause greater underreporting of risk-enhancing behaviors by BMSM; evidence suggests that correction for under-reporting leads to equivalent, rather than lower, risks for BMSM compared to WMSM.11
Race-assortative mixing (the tendency to select same-race partners) can enable disparities arising from other factors to remain concentrated within racial groups. Partner race is a strong explanatory factor in incidence studies,12,13 suggesting a role in facilitating persistence for existing disparities.6,7,10 However, race-assortative mixing alone likely cannot sustain a disparity arising from other sources. Modeling theory, including work on HIV/STIs, predicts that a given epidemic typically heads towards a fixed equilibrium, regardless of its current state.14 For each sub-population, this entails a specific prevalence representing the point at which incident infections are balanced by deaths among HIV-infected persons. We have built a web tool illustrating this to build further intuition in the context of a simpler model (https://sgoodreau.shinyapps.io/equilibria), and elaborated on the theory in the Supplement (pp. 22-25). The theory suggests that, if race-assortative mixing alone cannot generate a disparity, it also cannot sustain one indefinitely, let alone cause increasing disparities over time. However, exceptions to this theory exist, and epidemic dynamics can be slow for lifelong infections like HIV.15 The ability for reported levels of race-assortative mixing, in combination with other reported proximate factors, to generate observed HIV racial disparities among MSM or maintain them over the long timeframes they have been observed, has not been assessed. One study using data-driven models to examine this question for young MSM over 15 years found that racial differences in HIV incidence narrowed over the course of the simulated epidemic.16
Dynamic network models are well-positioned to evaluate multiple proposed mechanisms for their ability to generate or sustain disparities, and have been called for.7 We implemented a model parameterized by data from young MSM in Atlanta, Georgia to answer two questions:
Question 1. Assuming that disparities emerged from some unmeasured differences early in the epidemic, how long could they be sustained under observed race-assortative mixing, with or without other observed racial differences?
Question 2. How much of the observed 3 3-fold racial disparity in HIV prevalence could be generated by specific measured proximate factors, alone or in combination?
We further probe our findings for Question 2 to consider the potential impact of behavior misclassification induced by societal-level factors.
Methods
Model design
We used dynamic, stochastic network models that extend earlier work.17 We summarize key features here; the Online Supplement provides additional information. We began with 10,000 MSM, each possessing fixed (e.g. race, circumcision status, CCR532) and dynamic (e.g. age, infection status) attributes. HIV-infected men possessed additional dynamic attributes (e.g. diagnosis status, treatment status, stage, viral load). We simulated multiple transitions for each man by week, concurrently with relational dynamics (Table 1). Models simulated three contact networks for anal intercourse: main partnerships, casual partnerships with repeat contacts, and one-time contacts. These employed separable-temporal ERGMs,18 implemented in the R package suite statnet (www.statnet.org/trac/wiki) and EpiModel (www.epimodel.org). These methods allow fine control over numerous relational structures, including dependencies among having partners of each type; Table 2 includes modeled relational structures. Behavioral, demographic, and care-continuum parameters varied by race, or for counterfactual analyses were averaged across race. We modeled each scenario 16 times, with race-specific outcomes tracked until reaching equilibrium prevalence (Supplement p. 2).
Table 1
Process | Notes |
---|---|
Arrivals | Constant rate, equal number of Black and White MSM |
Departures due to aging out of model population | Occurs deterministically at age 40. |
Departures due to background mortality | Occurs with age- and race-specific all-cause mortality rates. |
Departures due to HIV mortality | Occurs as a complex function of time since infection off treatment, on treatment and fully suppressed, and on treatment and partially suppressed. |
Main and casual partnership formation | Modeled using ERGMs; occur in patterns that maintain the race- specific cross-sectional distribution of partner counts, race mixing and age mixing from our data. |
Main and casual partnership dissolution | Modeled using ERGMs; occur in patterns that maintain the race- specific partnership-type-specific relational durations. |
AI within main and casual partnership | Drawn each week for each relationship from a Poisson distribution with means specific to race combination and relational type. |
One-time AI contact | Drawn each week with probabilities that maintain race and age mixing, and with individuals’ one-time contacts a function of their race, count of ongoing partnerships, and individual propensity. |
HIV status disclosure | Modeled at the level of the relational pair; function of the races of the pair, relational type, and whether diagnosis occurs before or after start of relationship. |
Condom use | Determined separately for each act; function of races of pair, relation type, and diagnosis and disclosure status of HIV+ partner. |
Sexual role selection | Function of individual propensities, which vary by race; bi-directional AI allowed. |
Transmission | Probabilities depend on condom use; the positive partner’s infection stage, viral load and sexual position; and the negative partner’s circumcision status (if insertive) and CCR532 genotype. |
Viral dynamics | Largely follow those of previous models17 and depend on time since infection and treatment status. |
HIV testing | Small race-specific percentage of men never test; for those who do, intertest intervals are race-specific. |
Treatment initiation | Contingent on diagnosis; timing after diagnosis varies by race. |
Treatment cessation and re-initiation | Occur at race-specific rates in order to maintain reported prevalence of treatment and durable treatment by race. |
Suppression | May occur at partial or full levels, at race-specific frequencies. |
Table 2
Parameter | Black MSM value | White MSM value | Determinant Group | ||||||
---|---|---|---|---|---|---|---|---|---|
Proportion of men never testing | 7�7% | 5�2% | Care continuum | ||||||
Mean inter-test interval for men testing | 43 weeks | 45 weeks | Care continuum | ||||||
Percent linked to care within 3 months of diagnosis | 71�6% | 82�9% | Care continuum | ||||||
Proportion of diagnosed men who are on ART | 22�3% | 39�2% | Care continuum | ||||||
Proportion of currently suppressed men who are durably suppressed for 1 year | 58�8% | 69�1% | Care continuum | ||||||
Proportion of men on ART who are suppressed | 62�4% | 67�7% | Care continuum | ||||||
CCR532 genotype frequencies (32 homozygote, heterozygote, wildtype homozygote) | 0�0%, 3�4%, 96�6% | 2�1%, 17�6%, 80�3% | CCR532 | ||||||
Probability of disclosure of HIV+ status to new main partner at outset of relationship | 68�5% | 88�9% | Stigma-associated behaviors | ||||||
Probability of disclosure of HIV+ status to new casual partner at outset of relationship | 52�7% | 82�8% | Stigma-associated behaviors | ||||||
Probability of disclosure of HIV+ status to one-time contact | 44�5% | 69�1% | Stigma-associated behaviors | ||||||
Momentary degree distributions (i.e. proportion of men with a given number of main and casual relationships at a point in time) | 0 cas | 1 cas | 2 cas | 0 cas | 1 cas | 2 cas | Majority of sexual behaviors | ||
|
| ||||||||
0 main | 50�6% | 15�1% | 5�3% | 0 main | 43�5% | 18�4% | 9�5% | ||
|
| ||||||||
1 main | 20�7% | 6�1% | 2�2% | 1 main | 23�3% | 3�3% | 2�0% | ||
Mean number of one-time AI events per week, formen with a given momentary degree distribution | 0 cas | 1 cas | 2 cas | 0 cas | 1 cas | 2 cas | Majority of sexual behaviors | ||
|
| ||||||||
0 main | 0�073% | 0�091% | 0�080% | 0 main | 0�057% | 0�084% | 0�091% | ||
|
| ||||||||
1 main | 0�055% | 0�052% | 0�052% | 1 main | 0�057% | 0�058% | 0�058% | ||
Quintiles for mean number of of-time AI events per week | 0�00, 0�010, 0�039, 0�074, 0�212 | 0�00, 0�003, 0�036, 0�068, 0�231 | Majority of sexual behaviors | ||||||
Proportion exclusively insertive | 24�2% | 22�8% | Majority of sexual behaviors | ||||||
Proportion exclusively receptive | 32�1% | 22�8% | Majority of sexual behaviors | ||||||
Circumcision prevalence | 87�4% | 91�8% | Residual determinants | ||||||
Mortality rates (per year) | 0�00159 (18-24) | 0�00103 (18-24) | Residual determinants | ||||||
0�00225 (25-34) | 0�00133 (25-34) | ||||||||
0�00348 (35-39) | 0�00214 (35-39) | ||||||||
| |||||||||
Black-Black dyad value | Black-White dyad value | White-White dyad value | |||||||
| |||||||||
Mean main partnership duration | 348 days | 372 days | 555 days | Stigma-associated behaviors | |||||
Mean casual partnership duration | 131 days | 286 days | 144 days | Stigma-associated behaviors | |||||
Mean AI acts per week in main partnership | 1�19 | 1�79 | 1�56 | Majority of sexual behaviors | |||||
Mean AI acts per week in casual partnership | 0�75 | 1�13 | 0�98 | Majority of sexual behaviors | |||||
Base probability of condom use during AI, main partnership | 0�38 | 0�10 | 0�15 | Majority of sexual behaviors | |||||
Base probability of condom use during AI, casual partnership | 0�39 | 0�11 | 0�16 | Majority of sexual behaviors | |||||
Base probability of condom use during AI, one-time contact | 0�49 | 0�15 | 0�22 | Majority of sexual behaviors | |||||
Probability of intra-event role-versatility among two role-versatile MSM | 0�42 | 0�56 | 0�49 | Majority of sexual behaviors | |||||
Mean difference in square root of ages, main partnerships | 0�42 | 0�45 | 0�52 | Majority of sexual behaviors | |||||
Mean difference in square root of ages, casual partnerships | 0�50 | 0�63 | 0�63 | Majority of sexual behaviors | |||||
Mean difference in square root of ages, one-time contacts | 0�46 | 0�59 | 0�59 | Majority of sexual behaviors |
For sources and derivations, see Supplement (pp. 3-22)
Data sources
We obtained race-specific risk and prevention behaviors and network attributes from two studies of HIV/STI disparities in BMSM and WMSM in Atlanta, conducted 2010-2014 (Table 2). Involvement was a prospective HIV incidence cohort (n=803); the MAN Project was a cross-sectional chain-referral sexual networks study (n=314).4,13,19 Both used venue-time-space sampling (MAN Project for network-seed-level respondents). We included MSM from the baseline visit of Involvement and seeds from the MAN Project. Since these disparity-focused studies addressed MSM aged 18-39, the main timeframe over which HIV disparities appear, our model does also.
Participants completed self-administered computer-based questionnaires assessing demographics and prevention and risk behaviors for respondents and their most recent sex partners (Involvement, up to 5 in the previous 6 months; MAN Project, up to 10 in 12 months).4 We measured dyadic behaviors for pre-sexual, ongoing relationship, and last-sex time periods. Analyses included only dyads in which anal intercourse (AI) occurred at least once (n=2,626 dyads). We obtained treatment and efficacy parameters from published literature (Supplement p. 18). We adopted the susceptibility estimate for CCR532 heterozygotes from the one MSM-specific study,8 which found a stronger effect than heterosexuals studies have, because our model focused on MSM and our goal was to estimate maximum potential explanatory power for hypothesized factors.
BMSM reported lower levels of sexual risk behaviors, consistent with the literature and reports from the two source studies.3,4,13,19 However, BMSM’s mean relationship durations were consistently lower, as was serodiscussion, possibly reflecting differences owing to stigma.20 We thus combined sexual behaviors into two groups: stigma-associated behaviors that generally favor greater transmission for BMSM [Stigma-associated behaviors] and the remaining majority of sexual behaviors reported at higher levels by WMSM [Majority of sexual behaviors]. Table 3 outlines scenarios; all included race-assortative mixing at reported levels (~90% of relationships within-race across all partner types).
Table 3
Factor group Dash indicates factors set to between-race mean values Check indicates factors set to observed race-specific values | ||||||
---|---|---|---|---|---|---|
| ||||||
Description | HIV care continuum | CCR5 32 | Stigma-assoc behaviors | Majority of sexual behaviors | Residual determinants | |
| ||||||
Null (all factors set to between-race mean) | - | - | - | - | - | |
| ||||||
As-observed (all factors race-specific) | ||||||
| ||||||
Factor groups in isolation | Care continuum | - | - | - | - | |
| ||||||
CCR532 | - | - | - | - | ||
| ||||||
Stigma-associated behaviors (relationship duration, HIV serodiscussion) | - | - | - | - | ||
| ||||||
Majority of sexual behaviors | - | - | - | - | ||
| ||||||
Residual determinants (background mortality, circumcision rates) | - | - | - | - | ||
| ||||||
Combined | Biomedical determinants | - | - | - | ||
| ||||||
Care and disclosure a | - | a | - | - | ||
| ||||||
All sexual behaviors | - | - | - | |||
| ||||||
All BMSM risk factors | - | |||||
| ||||||
Misclassification of risk behaviors | BMSM assigned | |||||
WMSM values |
Note: all scenarios included observed race-assortative mixing
Our model does not aim to project the future, nor recreate past temporal trajectories, since the latter would require highly detailed historical data that do not exist. Rather, our aims are framed within a context where disparities in HIV burden have long existed at high levels, and where disparities in many determinants have been examined in isolation across various study designs. For Question 1, simulation duration has an explicit meaning, while for Question 2 we focus on equilibria, which represents the maximum disparity a scenario can generate or sustain. Intervention models often include a calibration step in which model parameter(s) are varied to obtain historical prevalence before beginning intervention rollout. Here, we instead by design estimated all parameters from data sources, without calibration, since the ability of observed factors to generate observed prevalence by race and the disparity between them is precisely the objective.
For Question 1, we specified networks in which BMSM and WMSM have 43% and 13% prevalence, respectively, matching that observed in Involvement.4 We set all behaviors for BMSM and WMSM to between-race mean values (Null scenario), with reported levels of race-assortativity. We repeated the simulations with all parameters set to observed race-specific values (As-observed).
For Question 2, we created initial networks in which BMSM and WMSM both have 5% HIV prevalence and conducted the simulation under all scenarios in Table 3. Every scenario included race-assortativity. By grouping the modeled factors and assigning either observed race-specific values or mean values averaged across races, we probed the disparity generated by all hypothesized factors together (As-observed), each factor group in isolation, and combinations of factor groups. The final scenario set BMSM sexual behaviors equal to WMSM, to probe the potential impact of misreporting owing to higher social desirability bias for minority respondents.11,21,22 We began these runs with low, equal prevalence to test whether these scenarios could generate a disparity. We then repeated select runs starting with observed race-specific prevalence to confirm the final disparity was insensitive to initial prevalence, as predicted by modeling theory (Supplement pp. 22-25).
Role of the Funding Source
The funder of the study had no role in study design, analysis, or interpretation, or in writing of the report. The corresponding author had access to de-identified data used in the study and had final responsibility for the decision to submit for publication.
Results
Figure 1a-b demonstrates the potential for observed levels of race-assortative mixing to sustain pre-existing HIV prevalence disparities, holding all other factors equal by race (Null scenario). Initially, incidence is higher for BMSM, given the difference in partners’ HIV prevalence. Subsequently, incidence and prevalence converge, with prevalence disparity reducing to half the initial disparity in 6 7 years, and by over 90% in 22 years. Figure 1c-d shows the same outcomes with all parameters set to observed race-specific values (As-observed). Disparities disappear even more quickly (prevalence disparity reducing by 50% and 90% in 3 3 years and 9 7 years, respectively), and equilibrium prevalence is higher for WMSM than BMSM.
Table 4 shows results for Question 2. Figure 2a-b shows the race-specific prevalences (x-axis=BMSM, y-axis=WMSM) for the same models, relative to observed Involvement results. Leaving all parameters As-observed by race yields mean BMSM and WMSM HIV prevalence values identical to those obtained using As-Observed parameters seeded with observed prevalences. The same is true for the Null scenario. This supports the extension of basic modeling theory to our more complex case; the set of race-specific equilibrium prevalences is independent of initial (non-zero) prevalence, and these scenarios cannot sustain pre-existing disparities larger than those they generate. We provide an additional example and detail in the Supplement (p. 22-25).
Table 4
Scenario Description | HIV prevalence, Black MSM | HIV prevalence, White MSM | HIV prevalence ratios (PR), Black MSM / White MSM | % of disparity explained | |
---|---|---|---|---|---|
Observed (Involvement cohort) | 197/454 = 43 4% [38 9% - 48 0%] | 46/349 = 13 2% [9 9% - 17 0%] | 3 30 [2 47 - 4 40] | ||
Null | 544/5578 = 9 8% [9 4% - 10 0%] | 556/5605 = 9 9% [9 2% - 10 3%] | 0 99 [0 92 - 1 04] | -0 4% [-3 5% - 1 8%] | |
As-observed | 558/5540 = 10 1% [9 5% - 10 3%] | 1078/5568 = 19 4% [19 1% - 0 0%] | 0 52 [0 50 - 0 54] | N/A | |
Factor groups in isolation | Care continuum | 609/5557 = 11 0% [10 6% - 11 7%] | 505/5630 = 9 0% [8 4% - 9 4%] | 1 23 [1 13 - 1 31] | 10 0% [5 7% - 13 7%] |
CCR532 | 694/5523 = 12 6% [12 0% - 13 5%] | 490/5593 = 8 8% [8 2% - 9 1%] | 1 44 [1 35 - 1 54] | 19 1% [15 4% - 23 5%] | |
Stigma-associated behaviors | 691/5552 = 12 4% [11 3% - 13 6%] | 542/5561 = 9 7% [9 1% - 10 3%] | 1 29 [1 17 - 1 41] | 12 7% [7 2% - 17 8%] | |
Majority of sexual behaviors | 703/5591 = 12 6% [12 0% - 13 0%] | 1708/5381 = 31 7% [31 1% - 32 4%] | 0 40 [0 37 - 0 41] | N/A | |
Residual factors | 612/5591 = 10 9% [10 6% - 11 4%] | 561/5596 = 10 0% [9 7% - 10 7%] | 1 10 [1 02 - 1 16] | 4 3% [1 0% - 6 8%] | |
Combined factor groups | Biomedical determinants | 844/5519 = 15 3% [14 4% - 16 2%] | 451/5630 = 8 0% [7 6% - 8 7%] | 1 93 [1 77 - 2 05] | 40 5% [33 7% - 45 5%] |
Care and disclosure | 696/5528 = 12 6% [12 1% - 13 2%] | 512/5601 = 9 1% [8 8% - 9 7%] | 1 38 [1 31 - 1 46] | 16 4% [13 6% - 20 0%] | |
All sexual behaviors | 638/5609 = 11 4% [11 0% - 11 8%] | 1572/5387 = 29 2% [28 6% - 29 5%] | 0 39 [0 38 - 0 40] | N/A | |
All BMSM risk factors | 1113/5423 = 20 5% [19 4% - 21 3%] | 508/5600 = 9 1% [8 4% - 9 4%] | 2 28 [2 18 - 2 36] | 55 5% [51 3% - 59 1%] | |
Misclassification of risk behaviors | 2255/5116 = 44 1% [43 9% - 44 0%] | 1635/5483 = 29 8% [29 2% - 30 7%] | 1 48 [1 45 - 1 53] | 20 8% [19 7% - 22 9%] |
— For observed values we report the mean and 95% confidence intervals.
— For simulated prevalence numbers, values represent the mean and interquartile range observed at the end of the 16 simulations, with initial population size of 10,000.
— Simulated prevalence ratios (PRs) are calculated separately at the end of each of the 16 runs; we report the mean and interquartile range of these 16 values.
— For the percent of disparity explained, we compare the mean and interquartile range of the 16 simulated prevalence ratios to the observed point estimate, using the formula (PRsim-1)/(PRobs-1). For scenarios in which all simulations yielded a larger HIV prevalence for White than Black men, we report the percent of disparity explained as not applicable (N/A). The percent of disparity explained for the Null model should be centered on 0, with stochasticity, by design.
We next consider the power of each factor group in explaining the observed disparity. Differences in the Care continuum, CCR532 and Stigma-associated behaviors each explained a substantial disparity. However, Majority of sexual behaviors did the opposite, yielding substantially lower BMSM prevalence. Racial differences in Residual determinants (circumcision, background mortality) yielded only a slight disparity. Most scenarios yielded WMSM prevalence close to observed prevalence, but Majority of sexual behaviors over-estimated it. BMSM prevalence was underestimated by all models.
Combining factor sets, close to half of the disparity was explainable by Biomedical determinants, and about one-sixth by Care and disclosure. All sexual behaviors yielded the same magnitude reverse disparity as Majority of sexual behaviors alone. The All BMSM risk factors scenario assessed a priori race-specific factor values that would favor higher BMSM prevalence; this yielded a large disparity, and is the only scenario to include individual runs with a disparity within the observed data’s confidence interval. The various factor sets generally yielded WMSM prevalence close to observed, except for All sexual behaviors. Some factor sets yielded BMSM prevalence values closer to observed prevalence than factor groups individually; all were still underestimates, however.
In a scenario probing Misclassification of risk behaviors, keeping all values As-observed by race but assigning BMSM equivalent to WMSM for Majority of sexual behaviors, mean BMSM prevalence closely matched the observed 43.4%. However, WMSM prevalence was higher than observed, generating a smaller-than-expected disparity.
Discussion
We used data from disparities-focused Atlanta studies and a previously-described model structure17 to ask whether the possible factors that have emerged in the scientific literature to date are sufficient to explain observed disparities. We find that these hypothesized explanatory factors accounted for, at most, 56% of observed disparities.
We demonstrated that, all else being equal, race-assortative mixing patterns in MSM cannot generate or sustain a racial disparity, let alone promote increased disparities over time. This suggests that race-assortativity along with pre-existing prevalence disparities is an insufficient explanation for population-level race disparities in incidence. However, it is important to distinguish the lack of population-level explanatory ability from that of prevalence among partner populations to predict short-term race-specific individual-level risk. In incidence studies13, HIV prevalence in specific partner pools (e.g., Black partners13, older partners12) remains a strong individual predictor of HIV acquisition. We urge that such associations not be used to stigmatize groups living with higher prevalence, but rather reinforce the urgent need for approaches that improve the health of everyone living with or at risk for HIV while respecting the dignity of all impacted communities.4,13,23
Lower HIV care engagement and attainment of viral suppression for BMSM explained a meaningful proportion of disparate HIV prevalence. Others have described the components of care and how social and structural factors, such as poverty and access to transportation, are associated with care outcomes.3 These findings underscore the urgency for achieving the U.S. National HIV/AIDS Strategy (NHAS) goal of increasing access to care, in order to achieve other NHAS goals to reduce new infections and HIV-related disparities.24 Prospective studies are needed to more fully understand reasons for racially-disparate HIV care outcomes among MSM and to develop interventions addressing the entire care continuum for MSM. Despite the importance of HIV care in explaining prevalence disparities,3,7 these disparities existed before the availability of effective combination treatments, supporting the contribution of other factors.25
We found that the CCR532 mutation had a large potential role in Black/White disparities among MSM. This hinged on the inclusion of a partially protective effect for the heterozygous genotype. A meta-analysis of predominantly small, diverse studies26 failed to show a consistent protective effect for heterozygosity, but importantly did not include the only US MSM and race-focused study that informed our parameter.8 Given the immutable nature of genetic differences, this presents a programmatic and messaging challenge. Because other groups with lower HIV prevalence (e.g., Asian-Americans) similarly lack deletion genotypes, the ability for genetic differences alone to impart HIV disparities is likely limited, but we demonstrate their potential to operate in concert with other factors to yield significant disparities.6
Relatedly, we did not consider STI as biological co-factors, given the additional model complexity and overlap with causal and associative mechanisms for HIV. Results from Involvement demonstrated high and similar racially-disparate incidence of gonorrhea, chlamydia, and syphilis, and statistical modeling estimated that rectal STI explained 15% of subsequent HIV infection.4,27 That work did not consider increased HIV transmission from co-infected MSM. Models incorporating STI might expect to yield greater racial HIV disparities, and are in development.
This analysis lends several new insights into the role of risk behaviors in MSM epidemics. Modeling only the set of risk behaviors at observed levels, generally reported at lower levels for BMSM, yielded an inverse disparity. That this disparity was nearly identical to the scenario with all factors As-observed shows the strong leverage for risk behaviors in determining disparities, which masks biomedical factors in creating disparities. When modeling equal, rather than lower, behavioral risks for BMSM relative to WMSM, the direction of disparity was reversed and population-observed BMSM prevalence was obtained, suggesting the potential for underreporting to help explain disparities. Racial differences in reporting have been discounted due to qualitative consistency of reports across time and geography, but recent validation studies have demonstrated underreporting.6,11,21,22 Further understanding of the social determinants of underreporting and development of risk biomarkers in MSM studies are needed.28 Two behaviors hypothesized as influenced by stigma – reduced HIV serodiscussion and shorter partnership duration – operated differently than other behaviors. When considered separately, they yielded a modest disparity, demonstrating a mechanism through which stigma can shape HIV risk for BMSM.
Our model did not explain the full magnitude of disparities observed in surveillance and research studies, and we suggest several possible sources not accounted for in our model, beyond STI. We incorporated the only published nationally-representative estimates of care engagement among MSM by race, derived from CDC datasources, because comparable Atlanta-specific data were unavailable; the latter might reflect greater racial differences in engagement and viral suppression. This highlights the need for improved care continuum surveillance for all racial groups of MSM at all jurisdictional levels. We modeled race-assortative mixing probabilities as uniform within race; individual heterogeneity in the propensity for inter-racial partnerships, attributable to residential or cultural segregation, may amplify disparities.
Although our model represents Black and White MSM in Atlanta, our initial population size (n=10,000) is smaller than those communities. However, models for infectious diseases like STIs where contact rates do not scale with population size are generally robust to size above a small threshold (on the order of n=1e3).29 Nevertheless, we re-ran our main scenario at n=5,000 and n=20,000 (results not shown), and confirmed that results were consistent.
Because this model focused on MSM <40 years old, disparity convergences were faster than would be observed in reality. We did not model HIV pre-exposure prophylaxis (PrEP), an effective biomedical prevention modality, since observed disparities pre-date PrEP approval. Elsewhere we extend our framework to consider PrEP,30 with subsequent work addressing PrEP’s impact on racial disparities.
High HIV incidence among BMSM is one of the most pressing public health concerns of our times and a national priority.24 Our model highlights the roles of access to effective HIV care, biological susceptibility, and behaviors, in influencing this risk. We demonstrate that there are residual sources of disparity that remain unexplained and demand further exploration, if we are to achieve health equity. Programs and policies must emphasize expanded access to antiretroviral therapies for HIV treatment and prophylaxis, while seeking to alleviate underlying social determinants and stigmas that shape BMSM’s HIV risks. Our results confirm that the goals of the US National HIV/AIDS Strategy, if achieved, hold substantial potential to reduce HIV disparities by race among MSM.
Acknowledgments
This project was funded by NIH research grants R21HD075662, RC1MD004370, R01MH085600, R01HD067111, R01AI083060, and R01HD068395. We were aided by additional institutional support came to the UW Center for AIDS Research (P30AI027757), the Emory Center for AIDS Research (P30AI050409), and the UW Center for Studies in Demography and Ecology (R24HD042828). We would like to thank study participants, the Statnet Development Team, the UW Network Modeling Group, the Helen R. Whitely Foundation and the Whitely Center.
Footnotes
Contributors:
SMG led the design and coding of the model, conducted some of the simulations, led the analysis of the simulations and co-led the writing.ESR contributed to the design of the model, led the analysis of the source data to parametrize the model, and co-led the writing.
SMJ contributed to the design and coding of the model, the analysis of the source data and simulations, and the writing.
NL contributed to the design of the model and the analysis of the source data to parametrize the model
SES conducted some of the simulations and contributed to the analysis of the results
GAM contributed to the conceptualization of the question, analysis and the writing
PSS contributed to the design of the model, the analysis of the source data and simulations, and the writing
Contributor Information
Steven M. Goodreau, Department of Anthropology, University of Washington, Seattle WA, USA.
Eli S. Rosenberg, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta GA, USA.
Samuel M. Jenness, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta GA, USA.
Nicole Luisi, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta GA, USA.
Sarah E. Stansfield, Department of Anthropology, University of Washington, Seattle WA, USA.
Gregorio A. Millett, amfAR, The Foundation for AIDS Research, New York, NY, USA.
Patrick S. Sullivan, Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta GA, USA. [Full professor]
References
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Funding
Funders who supported this work.
Emory Center for AIDS Research (1)
Grant ID: P30AI050409
NIAID NIH HHS (4)
Grant ID: P30 AI050409
Grant ID: R01 AI083060
Grant ID: P30 AI027757
Grant ID: UM1 AI069496
NICHD NIH HHS (4)
Grant ID: R24 HD042828
Grant ID: R01 HD067111
Grant ID: R21 HD075662
Grant ID: R01 HD068395
NIMH NIH HHS (2)
Grant ID: R01 MH085600
Grant ID: R21 MH112449
NIMHD NIH HHS (1)
Grant ID: RC1 MD004370
US National Institutes of Health (1)
Grant ID: grants R21HD075662, RC1MD004370, R01MH085600, R01HD067111, R01AI083060, and R01HD068395
University of Washington Center for AIDS Research (1)
Grant ID: P30AI027757
University of Washington Center for Studies in Demography and Ecology (1)
Grant ID: R24HD042828