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. Author manuscript; available in PMC: 2025 Jun 1.
Published in final edited form as: J Rural Health. 2023 Sep 27;40(3):419–429. doi: 10.1111/jrh.12799

Rural Veterans with HIV and Alcohol Use Disorder receive less video telehealth than urban Veterans

Alan Z Sheinfil 1,2,3, Giselle Day 1,2, Annette Walder 1,2, Julianna Hogan 1,2,3, Thomas P Giordano 2,4, Jan Lindsay 1,2,3,5, Anthony Ecker 1,2,3
PMCID: PMC10965503  NIHMSID: NIHMS1971829  PMID: 37759376

Abstract

Purpose:

Alcohol use disorder (AUD) is highly prevalent among Veterans with HIV. Rural Veterans with HIV are at especially high risk for not receiving appropriate treatment. This retrospective cohort cross-sectional study aimed to investigate patterns of mental health treatment utilization across delivery modality among Veterans diagnosed with HIV and AUD. It was hypothesized that rural Veterans with HIV and AUD would receive a lower rate of mental health treatment delivered via video telehealth than urban Veterans with HIV and AUD.

Methods:

A national Veterans Health Association administrative database was used to identify a cohort of Veterans diagnosed with HIV and AUD (N = 2,075). Geocoding was used to categorize rural Veterans (n = 246) and urban Veterans (n = 1,829). Negative binomial regression models tested associations between rurality and mental health treatment delivered via face-to-face, audio-only, and video telehealth modalities.

Findings:

Results demonstrated that rural Veterans with HIV and AUD received fewer mental health treatment sessions delivered via telehealth than urban Veterans with HIV and AUD (incidence rate ratio = 0.62; 95% confidence intervals [0.44, 0.87]; P < .01). No differences were found in terms of treatment delivered face-to-face or by audio-only.

Conclusions:

Rural Veterans with HIV and AUD represent a vulnerable subpopulation of Veterans who may most benefit from video telehealth. Efforts to increase access and improve the uptake of evidence-based mental health treatment delivered via video telehealth are needed.

Keywords: alcohol use disorder, HIV, rural mental health, telehealth, Veterans

INTRODUCTION

There are approximately 1.2 million people with HIV in the United States.1 People with HIV are at disproportionate risk for experiencing mental and behavioral health problems, including substance use disorder (SUD).2 Estimates suggest that as many as 48% of people with HIV engage in substance use behavior that puts them at risk for SUD,3 which is approximately 6.5 times greater than the risk for the general population. Alcohol use is particularly prevalent among people with HIV, with as many as 30%-50% of people with HIV meeting the criteria for alcohol use disorder (AUD).4 Unhealthy alcohol use (ie, >4/3 drinks for men/women per day and/or >14/7 drinks for men/women per week)5,6 among people with HIV is associated with increased risk for physiological harm7,8 and mortality.7 Alcohol use has also been shown to negatively impact each stage of the HIV care continuum, including lower rates of viral suppression and retention in care.9,10 To curb HIV transmission rates and, ultimately, end the HIV epidemic, national goals for successful HIV treatment include achieving 95% viral suppression across all people with HIV.11 Therefore, improving efforts to reduce unhealthy alcohol use among people with HIV represents a crucial clinical need.

The Veterans Health Administration (VHA) is the largest provider of HIV care in the United States, treating approximately 32,000 Veterans diagnosed with HIV during the 2022 fiscal year (September 2021-October 2022).12,13 Despite efforts within VHA to improve mental health treatment access and quality, gaps remain.14 For example, there are disparities in alcohol use treatment between Veterans with HIV and Veterans without HIV, such that Veterans with HIV who are unhealthy drinkers are 17% less likely to receive alcohol use treatment than Veterans without HIV who are unhealthy drinkers.12 These trends are even more pronounced among rural Veterans with HIV.15 Indeed, only 21%-24% of rural Veterans with HIV who screen positive for AUD receive treatment.15 As such, increasing access to mental health treatment for rural Veterans with HIV is critical. One potential method for addressing this treatment gap is through delivering treatment remotely, such as through telehealth.

Telehealth is broadly defined as the provision of health care delivered via telecommunication technologies.16 As it pertains to mental health care, common examples include treatment sessions conducted synchronously via video-to-video and audio-only (eg, telephone) appointments. Video telehealth, approved for use within VHA in 2013, allows providers to connect with, and deliver care to, Veterans in the comfort of their homes or other private location. Audio-only telephone is a more widely accessible remote care modality; however, it does not provide important visual, nonverbal information to providers assessing mental health symptomology.17,18 Evidence suggests that video telehealth is both feasible and acceptable for mental health care.19 In VHA, compared to in-person care, video telehealth for mental health treatment has been shown to yield equivalent therapeutic alliance, elicit high satisfaction from patients and providers, and be just as therapeutically effective.20–24 Moreover, there are no differences in the effectiveness of SUD treatment when delivered by video telehealth as compared to in-person. However, video telehealth is associated with greater patient satisfaction and higher levels of treatment retention than in-person SUD treatment.25

Mental health treatment delivered via video telehealth has the potential to address multiple barriers to accessing care, including those that disproportionately affect rural Veterans. For rural Veterans, the nearest VHA medical center, community-based outpatient facility, or specialty provider may be hundreds of miles away; and video telehealth can address logistical (eg, transportation) and psychosocial (eg, stigma) barriers.19 This has become even more germane in the context of the COVID-19 pandemic, as the need for remotely delivered services has drastically increased.18 As the COVID-19 pandemic progressed, Veterans reported elevated stress and anxiety, which may exacerbate substance use problems and increase the need for accessible mental health treatment.26 Indeed, people with HIV have reported increased substance use, including alcohol use, during the pandemic.26 Furthermore, pandemic-related factors have widened the gap in access to care for Veterans living in rural settings.27 For instance, there is evidence that, during the pandemic, rural Veterans did not keep pace with the uptake of telehealth for mental health treatment as compared to urban Veterans.28 However, less is known about mental health treatment utilization among Veterans diagnosed with HIV and AUD, leaving a gap in our understanding of how to best increase access to, and engagement with, these critical services.

The purpose of this study was to characterize the use of outpatient mental health treatment within VHA as a function of delivery modality and to compare utilization patterns between Veterans with HIV and AUD residing in rural versus urban settings. Based on long-standing29 and pandemic-related trends indicating decreased access to care for rural Veterans,27,28 it was predicted that rural Veterans diagnosed with HIV and AUD would receive fewer overall outpatient mental health treatment sessions than urban Veterans diagnosed with HIV and AUD, with the greatest discrepancy observed within sessions delivered by video telehealth. It was also predicted that this pattern would hold, even when comparisons were restricted to Veterans who received at least 1 mental health treatment session.

METHODS

This was a retrospective cohort cross-sectional study analyzing data obtained from a large-scale database of physical and mental health within VHA during fiscal year 2022 (October 2021-September 2022). The database is maintained and monitored for accuracy by the Veterans Affairs Information Resource Center. A national cohort of Veterans receiving VHA care was selected, based on the criteria of having documented both AUD and HIV-positive serostatus. HIV and AUD diagnoses were identified using the International Classification of Diseases-10 codes (ICD-10). Participants diagnosed with AUD were first identified, and those also diagnosed with HIV were then retained for the final sample. The remaining data were obtained after the point at which participants had AUD and HIV diagnoses documented.

Measures

Covariates included sample characteristics, mental health diagnoses, and substance and addictive disorders. The primary predictor was rurality, and the primary outcome was outpatient mental health treatment sessions.

Sample characteristics

Demographic information (ie, age, race/ethnicity, and gender), psychosocial factors (ie, homelessness), and HIV-related health (eg, HIV viral load and CD4 count) variables were extracted from the database. HIV viral load (HIV RNA copies/mL) is associated with substance use and mental health disorders2; as such, the most recent viral load test results were included as a covariate in the data analyses. Viral load was dichotomized as either undetectable (< 40 copies/mL) or detectable (≥ 40 copies/mL). Participants with HIV viral load test results that were unavailable (n = 183) or unable to be interpreted as detectable or undetectable (n = 24) were excluded from the cohort.

Mental health diagnoses

Co-occurring mental health diagnoses have well-documented associations with receipt of treatment.30–33 In addition, due to differing rates of engaging in mental health treatment in terms of specific mental health diagnoses34 and symptom severity associated with substance use and mental health disorder comorbidities,35 other mental health disorder and SUD diagnoses were entered as additional dichotomous covariates. Documented mental health disorder diagnoses were obtained from the database, as defined by the ICD-10 code. AUD was the only standalone diagnosis used to identify participants eligible to be included in the cohort. All other mental health or SUD diagnoses were identified by ICD-10 codes and collapsed into 1 of the 3 categories described next.

If participants had any of the following mental health disorders documented, they were categorized dichotomously as having a co-occurring mental health disorder diagnosis. Anxiety disorders were indicated by ICD-10 codes associated with a diagnosis of specific phobia, generalized anxiety disorder, social anxiety disorder, panic disorder, and agoraphobia. Depressive disorders were indicated by ICD-10 codes associated with a diagnosis of major depressive disorder or persistent depressive disorder. Trauma- and stressor-related disorders comprised ICD-10 code diagnoses associated with posttraumatic stress disorder, acute stress disorder, and adjustment disorder.

Substance and addictive disorders

If participants had any of the following SUD ICD-10 codes documented, they were categorized dichotomously as having a co-occurring SUD diagnosis: cannabis use disorder; opioid use disorder; stimulant use disorder; tobacco use disorder; and sedative, hypnotic, or anxiolytic use disorder.

Primary predictor—rurality

Participants were categorized as either rural or urban Veterans, based on a geocoding spatial intersection process developed by VHA.36 This procedure designates areas as urban or rural, with urban areas characterized by the US Census Bureau. Only 6 Veterans met the criteria for highly rural (ie, < 7 people per square mile). Therefore, highly rural and rural categories were combined to create a dichotomous variable used as the primary predictor in the data analysis.

Primary outcome—mental health treatment sessions

Mental health treatment sessions were identified by associated clinic codes indicative of the delivery of outpatient mental health care. Mental health treatment sessions were then categorized by delivery modality. The categories comprised face-to-face (ie, traditional in-person session), audio-only (eg, telephone session without any video), and video telehealth (ie, audio/visual synchronous remote session). These sessions were delivered by either a psychologist, psychiatrist, social worker, licensed chemical dependence counselor, licensed marriage and family therapist, or other health care provider (eg, primary care provider). The number of outpatient mental health treatment sessions received during fiscal year 2022 after documented diagnoses of AUD and HIV, stratified by each delivery modality, was used as the primary outcome variable.

Data analysis plan

Preliminary analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 28 (IBM SPSS Statistics). The criterion for statistical significance was set to an α level of 0.05. Descriptive statistics were used to summarize demographic characteristics, HIV care characteristics, and modality of mental health treatment. For continuous and count variables, means, medians, standard deviations, percentiles, and ranges were generated; frequencies and proportions were used for categorical variables. Welch-Satterthwaite independent-sample t-tests were used for unadjusted comparisons of mental health treatment sessions by delivery modality between rural and urban Veterans.

Primary analysis

For the count variable primary outcomes (ie, number of mental health treatment sessions), preliminary analyses revealed nonnormal distributions that were positively skewed, and approximated a negative binomial distribution. Therefore, generalized linear modeling was used to specify negative binomial regression models with robust standard error (SE) estimation to test the hypothesis that rural Veterans diagnosed with HIV and AUD would receive fewer number of mental health treatment sessions than urban Veterans. Because participants could have received mental health treatment delivered by a mix of 1 or more modalities, separate models were estimated for each delivery modality (ie, face-to-face, audio-only, and video telehealth) as a unique outcome.

Given the nonnormal distributions and overdispersion of each count outcome variable (face-to-face M = 21.02; variance = 1,922.17; dispersion parameter = 3.49; P < .001; audio-only M = 6.30; variance = 178.27; dispersion parameter = 2.28; P < .001; video telehealth M = 5.80; variance = 215.61; dispersion parameter = 4.31; P < .001), negative binomial distributions were modeled with log link functions.37 Rurality (dichotomous dummy-coded variable: rural = 1, urban = 0) was entered as the primary predictor of each model. All models were also adjusted for sex, age, race/ethnicity, homelessness, co-occurring mental health disorder, co-occurring SUD, and HIV viral load. Unstandardized coefficients (b), SEs, incidence rate ratios (IRRs), 95% Wald confidence intervals (95% CIs), and P values are reported. IRRs measure the percent change in count outcomes per 1-unit increase in the predictor or covariate. They can be interpreted as an effect size estimate.

Sensitivity analysis

Diagnostic data documented in medical records are subject to inaccuracies.38 For example, a Veteran could have previously met criteria for major depressive disorder, have undergone successful treatment, and no longer meet diagnostic criteria, and yet still have an outdated major depressive disorder diagnosis documented. However, it would not be indicated, nor expected, for this Veteran to receive additional mental health treatment. Therefore, a sensitivity analysis was conducted for a sample restricted to Veterans who received at least a single mental health treatment session by any treatment modality during the study. This restriction was used as an indicator of an accurately documented active mental health disorder for which treatment would be beneficial. In this analysis, it was hypothesized that the predicted pattern described in the primary analysis would hold. Specifically, rural Veterans diagnosed with HIV and AUD who attended a single mental health treatment session or more would receive fewer number of mental health treatment sessions delivered via video telehealth than urban Veterans. An identical analytic approach used in the primary analysis was applied for the sensitivity analysis; however, the sample was restricted to those who received at least a single mental health treatment session. These negative binomial regression models included the same set of covariates and outcomes used in the primary analysis.

RESULTS

Sample characteristics

Participants included in this analysis were 2,075 Veterans diagnosed with HIV and AUD. The average age was 56.41 years (SD = 12.22), with an average of 12.67 years (SD = 7.15) since first being diagnosed with HIV. Consistent with the broader VHA census, most participants were male (96%). Additionally, 61% were Black, 32% were White, and 8% were Hispanic/Latino. The most prevalent co-occurring mental health disorders were depressive disorders (52%), followed by trauma- and stressor-related disorders (39%), and then anxiety disorders (11%). Additionally, the sample was diagnosed with co-occurring stimulant use disorder (39%); cannabis use disorder (33%); tobacco use disorder (16%); and sedative, hypnotic, or anxiolytic disorder (2%). Table 1 contains demographic characteristics stratified by rurality.

TABLE 1.

Sample demographic characteristics by rurality.

Urban (n = 1,829) Rural (n = 246)
Demographics Categorical characteristics N (%) N (%) χ2(df) P-value
Sex 3.066 (1) .08
 Man 1,759(96.20) 242 (98.40)
 Woman 70 (3.80) 4 (1.60)
Ethnicity 7.19(2) .03
 Hispanic/Latinx 156 (8.50) 10 (4.10)
 Non-Hispanic/Latinx 1,601 (87.50) 222 (90.20)
Race 29.74 (5) <.001
 White 566 (30.90) 98 (39.80)
 Black/African American 1,137 (62.20) 123 (50.00)
 Asian 8 (0.40) 0 (0.00)
 American Indian/Alaska Native 16 (0.90) 10 (4.10)
 Native Hawaiian/Pacific Islander 15 (0.80) 3(1.20)
Homelessness 0.27(1) .61
 Yes 96 (5.20) 11 (4.50)
 No 1,733 (94.80) 235 (95.50)
Mental health disorder 0.01 (1) .93
 Depressive 942 (51.50) 127(51.60)
 Anxiety 199 (10.90) 24 (9.80)
 Trauma- and stressor-related 711 (38.87) 90 (36.59)
Other substance use disorder 0.23(1) .63
 Cannabis 610 (33.40) 75 (30.50)
 Stimulant 716(19.57) 92 (18.70)
 Opioid 168 (9.20) 28(11.40)
 Tobacco 290 (18.84) 41 (20.00)
 Sedative/hypnotic/anxiolytic 39 (2.10) 3(1.20)
Viral load (HIV RNAcopies/mL) 1.13(2) .57
 Undetectable (< 40) 1,404 (76.80) 185 (75.20)
 Virally suppressed (40–200) 200 (10.90)  25 (10.20)
 Virally unsuppressed (> 200) 225 (12.30) 36 (14.60)
CD4 count (mm3/mL) 7.51 (4) .11
 < 200 333 (18.20) 57(23.20)
 200–499 342 (18.70) 44 (17.90)
 ≥ 500 764 (41.80) 95 (38.60)
M (SD) M (SD) F (df) P-value
Age (years) 56.49(12.23) 55.84(12.16) 0.79 (315.40) .43
Years since HIV diagnosis 12.81 (7.14) 11.67 (7.14) 2.35 (314.49) .02
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Note: Total N = 2,075. Percentages may not sum to 100% due to missing data.

Unadjusted results

A total of 1,749 Veterans with HIV and AUD received mental health treatment. There was no difference in the proportion of rural (n = 199; 81%) compared to urban (n = 1,550; 85%) Veterans who received mental health treatment (χ2(1) = 2.42; P = .12). Most received treatment delivered by more than 1 modality (n Rural = 148; n Urban = 1,257) with the most frequent being audio-only (n Rural = 155; n Urban = 1,260), followed by face-to-face (n Rural = 158; n Urban = 1,254), and then video telehealth (n Rural = 108; n Urban = 958). The average number of mental health treatment sessions across all delivery modalities was 33.12 sessions (SD = 52.90). The highest average number of mental health treatment sessions was delivered face-to-face (M = 21.02; SD = 43.84), followed by audio-only (M = 6.30; SD = 13.35), and the lowest via video telehealth (M = 5.80; SD = 14.68). There were no unadjusted differences between rural and urban Veterans in average mental health treatment sessions across all delivery modalities (M Rural = 28.86; SD = 51.05; M Urban = 33.69; SD = 53.13), delivered face-to-face (M Rural face-to-face sessions = 19.42; SD = 40.59; M Urban face-to-face sessions = 21.24; SD = 44.27), or delivered audio-only (M Rural audio-only sessions = 5.77; SD = 22.47; M Urban audio-only sessions = 6.38; SD = 11.60). Compared to urban Veterans (M = 6.08, SD = 15.17), rural Veterans (M = 3.67, SD = 10.11) received significantly fewer sessions delivered via video telehealth (t(410.88) = 3.28, P < .001, 95% CI [0.96, 3.86], d = .16).

Primary results

Face-to-face mental health treatment

Results of a negative binomial regression model did not support the hypothesis that rural Veterans diagnosed with HIV and AUD received fewer mental health treatment sessions delivered face-to-face than urban Veterans diagnosed with HIV and AUD. The model provided good fit to the data (χ2(13) = 972.90; P < .001), yet there was no evidence of a significant association between rurality (M Rural face-to-face sessions = 27.18; M Urban face-to-face sessions = 31.60) and number of face-to-face mental health treatment sessions. However, documented experience of homelessness (b = 0.53; IRR = 1.70; 95% CI [1.32, 2.20]; P < .001), diagnosis of a co-occurring mental health disorder (b = 1.02; IRR = 2.78; 95% CI [2.22, 3.47]; P < .001), and diagnosis of a co-occurring SUD (b = 1.06; IRR = 2.87; 95% CI [2.32, 3.56]; P < .001) were associated with receiving a greater number of face-to-face mental health treatment sessions (Table 2).

TABLE 2.

Negative binomial regression models of mental health treatment delivery modality.

Face-to-face
 Telephone (audio only)
 Video telehealth (audlo/vldeo)
b(SE) IRR 95% CI P-value b (SE) IRR 95% Cl P-value b(SE) IRR 95% CI P-value
Age −0.008 (.01) 0.99 [0.98,1.00] .09 0.004 (.003) 1.00 [1.00,1.01] .26 −0.03 (.01) 0.97 [0.96,0.98] <.001
Sex 0.12 (.37) 1.12 [0.54, 2.33] .76 −0.24 (.17) 0.79 [0.57,1.10] .17 0.89 (.37) 2.43 [1.17, 5.06] .02
Race/ethnicity
 Black 0.30 (.12) 1.35 [1.08,1.69] .01 0.31 (.10) 1.36 [1.13,1.64] .001 −0.10 (.14) 0.91 [0.67,1.19] .48
 Hispanic/Latino 0.09 (.23) 1.09 [0.70,1.71] .70 −0.03 (.16) 0.98 [0.71,1.33] .87 −0.16 (.25) 0.85 [0.52,1.40] .52
 American Indian/Alaska Native 0.001 (.30) 1.00 [0.56,1.78] 1.00 −0.08 (.26) 0.92 [0.55,1.54] .76 −0.10 (.34) 0.91 [0.47,1.76] .77
 Native Hawaiian/Pacific Islander 0.39 (.49) 1.47 [0.56, 3.87] .43 0.02 (.41) 1.02 [0.46,2.28] .96 0.13 (.50) 1.14 [0.43, 3.06] .79
 Asian 1.08 (.94) 2.95 [0.47,18.45] .25 1.08 (.94) 2.96 [0.47,18.72] .25 −1.05 (.92) 0.35 [0.06, 2.15] .26
Homelessness 0.53 (.13) 1.70 [1.32, 2.20] <.001 0.39 (.14) 1.47 [1.12,1.93] <.01 −0.17 (.24) 0.84 [0.53,1.35] .48
Mental health disorder 1.02 (.11) 2.78 [2.22, 3.47] <.001 0.83 (.10) 2.23 [1.90,2.77] <.001 0.69 (.14) 2.00 [1.53, 2.62] <.001
Substance use disorder 1.06 (.11) 2.87 [2.32, 3.56] <.001 0.46 (.10) 1.58 [1.30,1.91] <.001 0.30 (.13) 1.36 [1.04,1.76] .02
HIV viral load 0.24 (.10) 1.28 [1.04,1.57] .02 0.04 (.09) 1.04 [0.87,1.24] .69 −0.39 (.14) 0.68 [0.51,0.89] <.01
Rurality
 Urban ref 1.00 − − ref 1.00 − − ref 1.00 − −
 Rural −0.15 (.14) 0.86 [0.66,1.12] .27 −0.17 (.22) 0.85 [0.55,1.01] .45 −0.48 (.18) 0.62 [0.44,0.87] <.01
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Note: N = 2,075. Male, White, no documented homelessness, no documented mental health disorder, undetectable HIV viral load, and no documented substance use disorder are categorical referents.

Abbreviations: 95%Cls, 95% Wald confidence intervals; IRR, incidence rate ratio.

Audio-only mental health treatment

Results of a negative binomial regression model did not support the hypothesis that rural Veterans diagnosed with HIV and AUD received fewer mental health treatment sessions delivered via audio-only than urban Veterans diagnosed with HIV and AUD. The model provided good fit to the data (χ2(13) = 383.58; P < .001), yet there was no evidence of a significant association between rurality (M Rural audio-only sessions = 5.35; M Urban audio-only sessions = 6.32) and number of audio-only mental health treatment sessions. Alternatively, documented experience of homelessness (b = 0.39; IRR = 1.47; 95% CI [1.12, 1.93]; P < .01), diagnosis of a co-occurring mental health disorder (b = 0.83; IRR = 2.29; 95% CI [1.90, 2.77]; P < .001), and diagnosis of a co-occurring SUD (b = 0.46; IRR = 1.58; 95% CI [1.30, 1.91]; P < .001) were associated with receiving a greater number of audio-only mental health treatment sessions (Table 2).

Video telehealth mental health treatment

Results of a negative binomial regression model demonstrated support for the hypothesis that rural Veterans diagnosed with HIV and AUD would receive fewer mental health treatment sessions delivered via video telehealth than urban Veterans diagnosed with HIV and AUD (Figure 1). The model provided good fit to the data (χ2(13) = 590.50; P < .001) and indicated that, compared to urban Veterans (M = 5.81), rural Veterans (M = 3.59) had a 38% decreased rate of receiving mental health treatment via video telehealth (b = −0.48; IRR = 0.62; 95% CI [0.44, 0.87]; P < .01). Additionally, female sex (b = 0.89; IRR = 2.43; 95% CI [1.17, 5.06]; P < .05), younger age (b = −0.03; IRR = 0.97; 95% CI [0.96, 0.98]; P < .001), a co-occurring mental health disorder diagnosis (b = 0.69; IRR = 2.00; 95% CI [1.53, 2.62]; P < .001), a co-occurring SUD diagnosis (b = 0.30; IRR = 1.36; 95% CI [1.04, 1.76]; P < .05), and an undetectable HIV viral load (b = 0.39; IRR = 1.48; 95% CI [1.128, 1.95]; P < .05) were all associated with receiving a greater number of video telehealth mental health treatment sessions (Table 2).

FIGURE 1.

FIGURE 1

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Mental health treatment by delivery modality.

Sensitivity analysis results

The results of the sensitivity analyses for which the sample was restricted to include only participants who received ≥ 1 mental health treatment session by any modality during the study duration (N = 1,749) were almost identical to results of the primary analysis, including the pattern and direction of the statistically significant relationships between variables (Table 3). The only significant association between rurality and mental health treatment was in the video telehealth model (b = −0.44; IRR = 0.64; 95% CI [0.46, 0.90]; P < .05). Also consistent with the primary analyses, this model demonstrated significant associations between video telehealth and age, gender, co-occurring mental health disorder, and detectable HIV viral load. Notably, having a co-occurring SUD was not significantly associated with video telehealth when the sample was restricted to Veterans with HIV and AUD who received ≥ 1 mental health treatment session.

TABLE 3.

Sensitivity analysis of negative binomial regression models of mental health treatment delivery modality.

Face-to-face
 Telephone (audio only)
 Video telehealth (audlo/vldeo)
b (SE) IRR 95% Cl P-value b(SE) IRR 95% Cl P-value b (SE) IRR 95% Cl P-value
Age 0.004 (.01) 1.00 [0.99,1.01] .43 0.01 (.003) 1.01 [1.00,1.02] .011 −0.03 (.004) 0.98 [0.97,0.98] <.001
Sex −0.05 (.32) 0.95 [0.51,1.77] .87 −0.30 (.16) 0.74 [0.54,1.01] .06 0.74 (.32) 2.10 [1.12,3.94] .02
Race/ethnicity
 Black 0.24 (.11) 1.27 [1.03,1.57] .03 0.23 (.09) 1.26 [1.05,1.50] .01 −0.16 (.14) 0.85 [0.65,1.12] .25
 Hispanic/Latino 0.08 (.21) 1.08 [0.71,1.64] .73 0.02 (.15) 1.02 [0.75,1.37] .91 −0.17 (.23) 0.85 [0.54,1.33] .48
 American Indian/Alaska Native 0.02 (.30) 1.02 [0.56,1.84] .96 −0.16 (.25) 0.86 [0.53,1.39] .53 −0.11 (.34) 0.90 [0.46,1.75] .75
 Native Hawaiian/Pacific Islander 0.29 (.47) 1.34 [0.53,3.36] .54 −0.12 (.38) 0.88 [0.42,1.87] .75 0.07 (.50) 1.08 [0.41,2.85] .88
 Asian 1.34 (.86) 3.80 [0.71,20.34] .12 1.35 (.86) 3.85 [0.71,20.81] .12 −0.72 (.81) 0.49 [0.10,2.38] .37
Homelessness 0.45 (.12) 1.56 [1.23,1.99] <.001 0.27 (.13) 1.31 [1.03,1.67] .03 −0.29 (.21) 0.75 [0.50,1.14] .18
Mental health disorder 0.68 (.11) 1.97 [1.58,2.45] <.001 0.48 (.09) 1.62 [1.35,1.94] <.001 0.35 (.14) 1.42 [1.09,1.86] .01
Substance use disorder 0.97 (.10) 2.62 [2.16,3.19] <.001 0.39 (.09) 1.48 [1.23,1.77] <.001 0.25 (.13) 1.28 [0.99,1.65] .06
HIV viral load 0.17 (.10) 1.19 [0.98,1.43] .07 0.001 (.09) 1.00 [0.85,1.18] .99 −0.43 (.14) 0.65 [0.50,0.86] .002
Rurality
 Urban ref 1.00 − − ref 1.00 − − ref 1.00 − −
 Rural −0.10 (.13) 0.90 [0.70,1.16] .43 −0.09 (.22) 0.92 [0.59,1.42] .70 −0.44 (.17) 0.64 [0.46,0.90] .01
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Note: N = 1,749. Male, White, no documented homelessness, no documented mental health disorder, undetectable HIV viral load, and no documented substance use disorder are categorical referents.

Abbreviations: 95%Cls, 95% Wald confidence intervals; IRR, incidence rate ratio.

DISCUSSION

This study demonstrated that rural Veterans diagnosed with HIV and AUD received fewer outpatient mental health treatment sessions via video telehealth than urban Veterans with HIV and AUD. Findings are consistent with COVID-19 pandemic trends that reveal existing disparities among rural Veterans in terms of lower rates of video telehealth for mental health treatment.28 Furthermore, rural Veterans with HIV and AUD were identified as a particularly vulnerable subpopulation of Veterans. Indeed, more than 50% of the sample met the criteria for a co-occurring depressive disorder, more than 75% had a documented co-occurring SUD, and more than 30% met the criteria for a co-occurring trauma- or stressor-related disorder. These elevated rates demonstrate the need for accessible mental and behavioral health treatment in the context of VHA HIV care,2,15 such as through integrated primary care models.39

There was no evidence of a significant relationship between rurality and receipt of mental health treatment via face-to-face and audio-only modalities, yet our findings suggest that rural Veterans are not maximizing potential benefits from remotely delivered mental health care. Given existing logistical and psychosocial barriers to engaging in mental health treatment for rural Veterans with HIV, receiving treatment via video telehealth has the potential to address major barriers to care. One potential explanation for the lower levels of video telehealth among this group is the limited availability of high-speed internet connectivity in rural settings, a necessity for undisrupted and efficient treatment delivery.40,41 Indeed, the COVID-19 pandemic exacerbated inequities in video telehealth for mental health treatment among those with suboptimal broadband.42 As such, targeted efforts to reduce logistical barriers and increase uptake of these services among those who stand to the most benefit are needed.

Other factors positively associated with receiving mental health treatment via video telehealth included younger age, female sex, and having an undetectable viral load. These findings are consistent with the broader literature in that individuals who are younger report more comfort with the technology needed to engage in mental health treatment remotely and the tendency for providers to more readily offer remote services to individuals they perceive to be proficient with this modality.43 This can be addressed within VHA through expanding the age range of dedicated programming and services to facilitate telehealth use, such as by providing older rural Veterans with free video-enabled tablet devices for mental health care.44

Female Veterans with HIV and AUD were more likely than male Veterans to receive mental health treatment via video telehealth; however, there was no evidence of sex differences in the receipt of treatment via face-to-face or audio-only modalities. Within VHA, this finding may be attributed to female Veteran experiences of genderbased discrimination, sexual harassment, and caregiver/household responsibility burdens.45–47 As observed in other VHA studies,47 if presented with an opportunity to engage in treatment remotely from the safety and comfort of their home, female Veterans may be more amenable to this treatment modality. This tendency can further contribute to female Veterans’ capability in navigating mental health treatment delivered via telehealth.

A detectable HIV viral load was associated with less video telehealth treatment. To our knowledge, this study is the first to demonstrate a relationship between an indicator of successful HIV treatment and mental health treatment via video telehealth. Nevertheless, it is somewhat consistent with previous work indicating that Veterans with a detectable viral load receive a higher level of AUD treatment than Veterans with an undetectable viral load.48 This may be due in part to clinicians’ desire to encourage regular in-person contact with Veterans who have a detectable viral load in an effort to closely monitor HIV treatment progress with laboratory testing and to promote consistent viral suppression through in-person adherence counseling. Although these individual-difference characteristics were associated with video telehealth treatment, other mental health comorbidities were associated with all 3 delivery modalities.

Comorbid mental health and SUDs can diminish treatment effectiveness and, thus, often call for more intensive treatment approaches.30–33 The current study extends this work through the observation of strong associations between having a documented comorbid mental health disorder and/or a comorbid SUD, with an increased rate of mental health treatment across all delivery modalities. Within VHA, Veterans with posttraumatic stress disorder and AUD express preferences for concurrent treatment approaches (ie, treatment that addresses multiple disorders simultaneously),49 yet evidence suggests a low proportion of Veterans engaged in mental health treatment receive these types of interventions.50–52 Alternatively, from an HIV care provider’s perspective, clinicians may not perceive themselves as competent to provide mental health and/or substance use treatment and instead elect to refer patients out to specialty care.12 These practices create a gap in connecting Veterans with HIV to mental and behavioral health services. Moreover, treatment for AUD may be perceived as less accessible in rural settings due to resource conservation and limited availability of providers with clinical expertise.53 Notably, delivering treatment via video telehealth has the potential to ameliorate these barriers, despite some provider-driven obstacles.54–56 Regardless of the explanation, these findings underscore the prevalence of co-occurring mental health disorders and SUDs among Veterans with HIV, indicating a crucial need for evidence-based transdiagnostic interventions that can address core processes underlying multiple comorbid disorders. Future research should investigate the effectiveness of these interventions tailored to this subpopulation when delivered via telehealth.

Findings from this study should be considered within the context of its limitations. First, to define the cohort of Veterans included in the sample, we relied on ICD-10 codes documented in electronic medical records, which are subject to inaccuracies due to user error.38 Therefore, it is possible that Veterans included in the sample did not meet the criteria for AUD and/or were not diagnosed with HIV. Nevertheless, consistent with other validated procedures for using electronic medical records to identify Veterans with HIV,57 we restricted our sample to Veterans who had at least 2 data points indicative of an accurate HIV diagnosis (ie, ICD-10 code and recent HIV viral load lab result). This approach reduced the risk of inadvertently including Veterans without HIV. Second, this study was not able to assess the content, quality, or outcomes of the type of mental health treatment delivered by each modality. For example, it is possible that even though Veterans were diagnosed with AUD, they may not have received an evidence-based treatment that successfully targeted or reduced AUD symptoms. Future studies should consider incorporating an indicator of treatment quality and outcomes when investigating delivery modality among Veterans with HIV and AUD. This is especially important considering evidence suggesting no differences in terms of SUD treatment effectiveness when delivered via video telehealth.25 Third, rather than using a finer-grained measure of the rurality continuum, a dichotomous indicator of rurality was used as the primary predictor. This type of indicator does not account for the heterogenous nature of rural settings that vary across different geographic regions (eg, rural Southwest vs rural Northeast). Future studies should consider employing a more fine-grained measure of rurality that can provide more nuanced insights regarding the range of rural experiences among Veterans. Lastly, given that this sample comprised only Veterans with HIV enrolled in VHA care, it is possible that findings do not generalize to other persons with HIV and AUD, such as those who receive services from community care providers. Future studies should aim to collect information from a diverse range of people with HIV and AUD in an effort to elucidate mental health treatment gaps across a variety of psychosocial characteristics.

This national study of Veterans with HIV and AUD demonstrated that, compared to urban Veterans, rural Veterans received less mental health treatment delivered via telehealth. Increased attention is needed to improve access and uptake of mental health treatment for rural Veterans with HIV. Video telehealth can mitigate mental health treatment disparities affecting rural Veterans. Research to investigate effective interventions and delivery approaches for this vulnerable subpopulation of Veterans with HIV is warranted.

FUNDING INFORMATION

This research is supported by the Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment, the Medical Research Service of the Michael E. DeBakey Veterans Affairs Medical Center, and the Department of Veterans Affairs South Central Mental Illness Research, Education, and Clinical Center (SC MIRECC). This work is supported by a grant from the VA Office of Rural Health, Veterans Rural Health Resource Center-Salt Lake City to Drs. Lindsay, Ecker, and Hogan and partly the result of the use of facilities and resources of the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN13–413) and the VA SC MIRECC which played no role in the design and conduct of the work; in the collection, analysis, and interpretation of the data; and in the preparation, editing, or censuring of the manuscript. TPG is supported by the Texas Developmental Center for AIDS Research (P30AI161943) and MD Anderson Foundation Chair at Baylor College of Medicine. The opinions expressed are those of the authors and do not necessarily reflect those of the Department of Veterans Affairs, the US Government, or Baylor College of Medicine.

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

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