Intersectionality-Informed Analysis of Durable Viral Suppression Disparities in People With HIV

Adovich S. Rivera; Laura K. Rusie; Matthew J. Feinstein; Juned Siddique; Donald M. Lloyd-Jones; Lauren B. Beach

AIDS. 2023;37(8):1285-1296.

Abstract and Introduction

Abstract

Objective: The aim of this study was to examine drivers of durable viral suppression (DVS) disparities among people with HIV (PWH) using quantitative intersectional approaches.

Design: A retrospective cohort analysis from electronic health records informed by intersectionality to better capture the concept of interlocking and interacting systems of oppression.

Methods: We analyzed data of PWH seen at a LGBTQ federally qualified health center in Chicago (2012–2019) with at least three viral loads. We identified PWH who achieved DVS using latent trajectory analysis and examined disparities using three intersectional approaches: Adding interactions, latent class analysis (LCA), and qualitative comparative analysis (QCA). Findings were compared with main effects only regression.

Results: Among 5967 PWH, 90% showed viral trajectories consistent with DVS. Main effects regression showed that substance use [odds ratio (OR) 0.56, 0.46–0.68] and socioeconomic status like being unhoused (OR: 0.39, 0.29–0.53), but not sexual orientation or gender identity (SOGI) were associated with DVS. Adding interactions, we found that race and ethnicity modified the association between insurance and DVS (P for interaction <0.05). With LCA, we uncovered four social position categories influenced by SOGI with varying rates of DVS. For example, the transgender women-majority class had worse DVS rates versus the class of mostly nonpoor white cisgender gay men (82 vs. 95%). QCA showed that combinations, rather than single factors alone, were important for achieving DVS. Combinations vary with marginalized populations (e.g. black gay/lesbian transgender women) having distinct sufficient combinations compared with historically privileged groups (e.g. white cisgender gay men).

Conclusion: Social factors likely interact to produce DVS disparities. Intersectionality-informed analysis uncover nuance that can inform solutions.

Introduction

Achieving durable viral suppression (DVS) is an important treatment milestone for people with HIV (PWH).^[1] DVS translates to benefits such as lower risk for HIV-associated chronic conditions and population benefits, as virally suppressed individuals are highly unlikely to transmit HIV.^[2–5] Achieving DVS is a key strategy in the US Ending the HIV Epidemic plan.^[6] Unfortunately, the achievement of one-time viral suppression in the United States remain sub-optimal at nearly 65%.^[7,8] Importantly, one-time viral suppression disparities by race-ethnicity, sex, socioeconomic status (SES), and other demographic variables exist.^[9,10]

These disparities are largely due to social factors. Poor viral suppression is associated with society-inflicted adverse conditions such as having depression, living in economically deprived areas, or facing difficulty in meeting daily needs.^[9,11,12] HIV viral suppression disparities are produced by the complex interactions of health and societal systems with intersectional social position and identities among PWH populations.^[9,13] Despite this underlying theory, research typically focused on isolating the role of a single social determinant of health (SDOH) or examining disparities through a single axis of identity (e.g. just race).^[13,14] In turn, policy recommendations often target singular issues or population groups.

There is an increasing call within public health to apply intersectionality in health research.^[13–15] An intersectional approach attempts to address the complexity of how interlocking social determinants and systems of oppression produce disparities seen at the population level, and investigates the unique impacts of these interacting forces on each person holding multiply-constituted social identities.^[16,17] Applying this approach better attends to the fact that many PWH hold multiple marginalized identities (e.g. Hispanic bisexual cisgender women) and PWH often face multiple adverse socioeconomic conditions caused by historical, generational, and present-day stigma and oppression.^[9,13,18,19] By embracing this complexity, new insights may be gained, which can guide development of interventions to address persistent health disparities.^[14]

Here, we conducted an intersectionality-informed quantitative analysis to investigate the association of SDOH-related factors with DVS. We drew extensively from the work of health scholars engaged in integrating quantitative methods with intersectionality.^[20–22] Our goal is to uncover new insights to inform improvement in policy and implementation of the HIV elimination plan for the USA.^[6,23] We demonstrate intersectionality-informed analysis using electronic health records (EHRs) data that can be used for guiding HIV elimination globally.^[24]

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Next Section

Abstract and Introduction
Materials and Methods
Results
Discussion
References

Table 1. Comparison of people who achieved durable viral suppression to those who did not achieve DVS (non-DVS), Howard Brown Health, 2012–2019 ( n = 5967).
	No durable viral suppression	Durable viral suppression
n	659	5308
Age at baseline [mean (SD)]	39.15 (11.11)	43.43 (12.62)
Not assigned male at birth (%)	121 (18.4)	935 (17.6)
Sex identity (%)
cisgender man	559 (85.6)	4637 (88.4)
cisgender woman	32 (4.9)	268 (5.1)
transgender woman	60 (9.2)	291 (5.5)
transgender man, genderqueer, or else^a	2 (0.3)	51 (1.0)
Sexual orientation (%)
Bisexual	62 (9.8)	344 (6.8)
Gay or lesbian	480 (76.2)	4006 (79.7)
Heterosexual	73 (11.6)	540 (10.7)
Queer or else^b	15 (2.4)	139 (2.8)
Race/ethnicity (%)
Black, non-Hispanic	304 (47.2)	1691 (32.5)
Hispanic	128 (19.9)	1166 (22.4)
Others, non-Hispanic^c	33 (5.1)	258 (5.0)
white, non-Hispanic	179 (27.8)	2089 (40.1)
Insurance class (%)
Private	141 (21.4)	2538 (47.8)
Government	328 (49.8)	1761 (33.2)
Uninsured or others^d	190 (28.8)	1009 (19.0)
Housing status at baseline (%)
Unhoused	79 (12.0)	196 (3.7)
Nonpermanent/Institution	109 (17.9)	550 (11.2)
Permanent	421 (63.9)	4164 (78.4)
Poverty category (%)
≤100% FPL	478 (72.5)	2887 (54.4)
>100 to ≤200% FPL	93 (14.1)	869 (16.4)
>200% FPL	88 (13.4)	1548 (29.2)
Comorbidities
Mental health disorder (%)	321 (48.7)	2241 (42.2)
Substance abuse disorder (%)	239 (36.3)	1261 (23.8)
Diabetes or hypertension (%)	27 (4.1)	361 (6.8)
HIV-related variables
Year of HIV onset (median [IQR])	2012 [2008, 2015]	2012 [2007, 2016]
HIV onset before 2012 (%)	293 (44.5)	2403 (46.4)
Year of first viral load (median [IQR])	2014 [2012, 2016]	2015 [2012, 2017]
Year of last viral load (median [IQR])	2019 [2018, 2019]	2019 [2018, 2019]
Duration of first to last viral load (year, median [IQR])	3.14 [1.83, 6.02]	2.79 [1.47, 5.52]
Total viral load tests (mean [SD])	7 [4, 11]	8 [5, 13]
Number of viral load <100 copies/ml (median [IQR])	2 [0, 4]	7 [4, 11]
Proportion of total viral load <100 copies (mean (SD))	28 (23)	86 (19)
Viral load (copies/ml) (median [IQR])⁵
First viral load	37 280 [34 160, 100 000]	20 [20, 16 922]
Last viral load in first year	4440 [50, 53 454]	20.00 [20, 20]
Last viral load ever	2660 [32, 44 750]	20 [20, 20]
Viral load <100 copies/ml (%)
First viral load	75 (11.4)	3172 (59.8)
Last viral load in first year	203 (30.8)	4918 (92.7)
Last viral load ever	236 (35.8)	4988 (94.0)

FPL, federal poverty line; IQR, interquartile range; SD, standard deviation.
^aIncludes genderqueer, queer, nonbinary, and all other responses except missing or refused (n = 67).
^bIncludes genderqueer, gender nonconforming and replied 'something else' when asked about sexual orientation except missing or refused (n = 308).
^cOther race and ethnicity variables include Asian, Pacific Islander, Multiracial, and Unspecified.
^dOther insurance categories include entries like walk-in, sliding scale, or grant.

Table 2. Association of selected socioeconomic status indicators with durable viral suppression, Howard Brown Health, 2012–2020.
Variable	Odds ratio (95% confidence interval)
Variable	Complete case	Imputed Dataset
Poverty (ref: Above 200% FPL)^b
Between 101 and 200% FPL	0.58 (0.41–0.80)^a	0.56 (0.41–0.75)^a
100% FPL and below	0.36 (0.28–0.46)^a	0.37 (0.29–0.47)^a
Housing status (ref: Has housing)^c
Unhoused	0.39 (0.29–0.53)^a	0.41 (0.31–0.55)^a
Insurance (ref: Private insurance)^d
Government insurance	0.38 (0.3–0.49)^a	0.39 (0.31–0.50)^a
Uninsured	0.38 (0.29–0.50)^a	0.40 (0.31–0.51)^a
Mental Health (ref: No disorder)^e
With disorder	0.86 (0.71–1.04)	0.86 (0.72–1.03)
Substance Use disorder (ref: No disorder)^e
With disorder	0.56 (0.46–0.68)^a	0.58 (0.49–0.70)^a

FPL, federal poverty line.
^aConfidence interval does NOT cross null (1.0). Odds ratios were calculated using multivariable regression. Each socio-economic variable has a
separate model.
^bPoverty models adjust for age, year of first viral load, race and ethnicity, sexual orientation, gender identity, poverty status. Housing and insurance are mediators, thus, were exclude from the model.
^cHousing model adjusts for variables in poverty model and housing status. Poverty is a confounder, but insurance is a mediator, so insurance is excluded from the model.
^dInsurance model adjusts for variables in poverty model as well as housing, poverty, and insurance status. Poverty and housing status are confounders, so they were included.
^eMental health and substance use disorders use the same model which adjusts for age, year of first viral load, race and ethnicity, sexual orientation, gender identity, diabetes or hypertension, mental health disorder, substance use disorder, and poverty status. Insurance and housing were mediators of poverty so were excluded.

Table 3. Disparities in durable viral suppression according to selected demographic categories, Howard Brown Health, 2012–2020.
Category	A. Nonintersectional analysis (Odds ratio, 95% confidence interval)
	Complete case			Imputed data
	Model 1^d	Model 2^d	Model 3^d	Model 1^d	Model 2^d	Model 3^d
A. Race & ethnicity (ref: White, non-Hispanic)
Hispanic	0.97 (0.75–1.25)	0.99 (0.77–1.29)	0.99 (0.76–1.29)	0.91 (0.71–1.15)	0.91 (0.72–1.17)	0.92 (0.72–1.18)
Asian/other, non-Hispanic^a	1.22 (0.7–2.3)	1.24 (0.71–2.35)	1.15 (0.65–2.2)	0.92 (0.62–1.42)	0.93 (0.62–1.43)	0.93 (0.62–1.45)
Black, non-Hispanic	0.6 (0.48–0.73*	0.63 (0.51–0.79*	0.73 (0.58–0.93)^e	0.57 (0.46–0.69)^e	0.58 (0.47–0.71)^e	0.68 (0.54–0.84)*
B. Gender identity (ref: Cisgender man)
Cisgender woman	0.76 (0.51–1.16)	0.96 (0.59–1.6)	1.05 (0.64–1.76)	0.87 (0.61–1.3)	1.04 (0.66–1.68)	1.16 (0.73–1.86)
Transgender woman	0.66 (0.48–0.92)	0.76 (0.54–1.09)	1.09 (0.76–1.57)	0.72 (0.54–0.98)	0.82 (0.59–1.14)	1.12 (0.8–1.57)
Transgender man, genderqueer, or else^b	5.57 (1.2–99)^e	5.3 (1.12–95)^e	6.34 (1.31–114)^e	3.58 (1.1–22)^e	3.5 (1.06–22)^e	3.74–23)*
C. Sexual orientation (ref: Heterosexual/Straight)
Bisexual	0.9 (0.61–1.32)	0.79 (0.52–1.21)	0.84 (0.55–1.3)	0.85 (0.59–1.23)	0.76 (0.51–1.14)	0.83 (0.56–1.24)
Gay or Lesbian	1.37 (1.03–1.81)^e	1.07 (0.75–1.52)	0.96 (0.67–1.36)	1.2 (0.93–1.54)	0.95 (0.68–1.31)	0.86 (0.62–1.18)
Queer or else^c	1.82 (0.99–3.56)	1.49 (0.79–2.98)	1.38 (0.73–2.8)	1.57 (0.9–2.93)	1.3 (0.73–2.48)	1.21 (0.67–2.33)
Category		B. Intersectional analysis with latent class
		Complete case			Imputed data
		Model 4^d	Model 5^d		Model 4^d	Model 5^d
Reference: A (mixed r/e, mixed SES, gay, cis men)
B (majority Black, low SES, cis, het men/women)		0.37 (0.26–0.52)^e	0.45 (0.33–0.63)^e		0.46 (0.33–0.64)^e	0.45 (0.33–0.63)^e
C (mixed r/e, low SES, bi/gay, cis men)		0.31 (0.25–0.38)^e	0.41 (0.34–0.49)^e		0.38 (0.32–0.46)^e	0.41 (0.34–0.49)^e
D (mixed r/e, low SES, mixed sexual orientation, trans women)		0.32 (0.23–0.46)^e	0.43 (0.31–0.6)^e		0.40 (0.29–0.57)^e	0.43 (0.31–0.6)^e

DM, diabetes mellitus; HTN, hypertension; LCA, latent class analysis; r&e, race and ethnicity; SES, socioeconomic status; SO, sexual orientation; TG, transgender.
^aOther race and ethnicity variables include Asian, Pacific Islander, Multiracial, and Unspecified.
^bIncludes genderqueer, gender nonconforming or replied ''something else'' when asked about gender. Pooled with trans man due to low numbers.
^cOther sexual orientations include Queer, Questioning, and Something else responses.
^dEachmodel adjusts for slightly different variables. Models 1, 2, and 4 adjust only for confounders with model 2 assuming demographic variables as confounders of each other. Model 3 and 5 adjusts for mediators of demographic variables. Specific variables are specified as follows:
Model 1: adjusts only for age and year of first viral load. Three separate models, one for each demographic variable.
Model 2: One model that adjusts for age, year of first viral load, and demographic categories.
Model 3: One model that adjusts for variables in model 2 as well as mediator variables including housing, poverty, insurance, diabetes or hypertension status, mental health disorder, and substance use disorder.
Model 4: Similar to model 1 in that it adjusts for age and year of first viral load.
Model 5: Similar to model 3 in that it adjusts for age and year of first viral load as well as mediators, which include hypertension status, mental health disorder, and substance use disorder.
^eConfidence interval does NOT cross null (1.0).