Abstract
Objective:
Prescribing of ADHD medications has not been examined in the US homeless population. This study examined frequency of prescriptions for stimulant and non-stimulant ADHD medications, as well as risky and potentially inappropriate prescribing (RPIP) of stimulants, in three groups of veterans with mental illness.
Methods:
Using 2021–2022 national VA administrative data, we compared frequency of stimulant and non-stimulant ADHD medication prescriptions using logistic regression between homeless veterans (n = 105,062), veterans in the Department of Housing and Urban Development-VA Supportive Housing (HUD-VASH; n = 33,884), and independently housed (IH) veterans (n = 1,875,083). We also compared indicators of RPIP of stimulants using chi-square tests between the three groups.
Results:
Adjusted for sociodemographic, clinical, and health care utilization characteristics, homeless veterans were less likely to be prescribed stimulants (adjusted odds ratio (aOR) = 0.80, 99% CI = 0.76–0.85) relative to IH veterans and more likely to be prescribed non-stimulants (aOR = 1.12, CI = 1.06–1.18). However, among veterans prescribed stimulants, homeless veterans had more indicators of RPIP, including being prescribed stimulants in the presence of a psychotic disorder (7.9% vs. 6.4% for HUD-VASH vs. 2.2% for IH, p <.001).
Conclusion:
Homeless veterans with mental illness were less likely to be prescribed stimulants and more likely to be prescribed non-stimulant ADHD medications relative to IH veterans with mental illness. However, RPIP of stimulant prescriptions was more common for homeless and HUD-VASH veterans relative to IH veterans.
Keywords: Homeless, veterans, ADHD, stimulants
Background
Despite a burden of psychiatric illness that is among the highest of any group (Gutwinski et al., 2021), little is known about psychiatric medication prescribing in homeless populations. In particular, though evidence suggests high rates of inattention and Attention Deficit Hyperactivity Disorder (ADHD) in homeless populations (Lomas et al. 1997, Burra et al. 2009), no U.S. study has examined prescribing of stimulant and non-stimulant ADHD medications among this group. This question is especially relevant given that stimulant prescribing and misuse has increased nationally among the general population in recent decades (Danielson et al., 2023; Han et al., 2025).
Stimulant medications, including amphetamine/dextroamphetamine (Adderall) and methylphenidate (Ritalin), are medications approved for ADHD by the FDA. Stimulants are also FDA-approved to treat narcolepsy and used off-label for treatment-resistant depression (Corp et al., 2014). These medications have effectiveness and tolerability (Wilens et al., 2008), but also potential for misuse, owed to higher degrees of euphorigenic effects relative to non-stimulant ADHD medications (Jasinski et al., 2008). Prescribed stimulant misuse poses substantial health risks including myocardial infarction and cerebrovascular complications such as stroke, psychosis, and seizures (NIH National Library of Medicine, 2025; Batki et al., 2023; Moran et al. 2024). As a result of these risks, clinicians often consider non-stimulant ADHD medications including atomoxetine, clonidine, guanfacine, and bupropion (Chai et al., 2024).
When prescribing for homeless patients, clinicians need to be especially prudent to maximize appropriate treatment in the context of high rates of substance use. Evidence suggests that illicit stimulant use, such as cocaine and methamphetamine, is common and increasing among homeless populations (Assaf et al., 2025), perhaps related to its ability to improve alertness and productivity while homeless (Damon et al., 2019; Fast et al. 2014). Prescribed stimulants have been found to increase the effects of crystal meth or cocaine (Kecojevic et al., 2015). Caution in prescribing stimulants is therefore advised for people with methamphetamine use disorder, stimulant use disorder, and psychotic disorders (NIH National Library of Medicine, 2024), which are already high in the homeless population (Gutwinski et al., 2021), as well as in people with certain medical conditions such as cardiovascular disease, hyperthyroidism, glaucoma, motor tics, agitated states, tourette’s disorder, and a concurrent MAO-I prescription (Corp et al., 2014; NIH National Library of Medicine, 2025; Tadrous et al., 2021). This risky and potentially inappropriate prescribing (RPIP) of stimulants can increase adverse outcomes for homeless patients who are already psychiatrically, medically, and socially vulnerable.
A greater understanding of ADHD medication prescribing and RPIP can help inform safe medication prescribing for this population. The U.S. Department of Veterans Affairs (VA) is the country’s largest provider of homeless services (Tsai et al., 2021). The current study therefore used national VA medical records from 2021 and 2022 to 1) compare frequency of prescribing of stimulant and non-stimulant ADHD medications in three groups of veterans with mental illness: those currently homeless, those in supported housing through the U.S. Department of Housing and Urban Development-VA Supportive Housing (HUD-VASH) program, and independently housed (IH) veterans and 2) identify RPIP of stimulants among these groups. Having three groups allows for comparisons of veterans in incremental stages of housing stability and testing of graded hypotheses. We hypothesized that homeless veterans would be prescribed stimulants at a higher rate than the two groups of housed veterans, given that they may be more likely to ask for stimulants as well as the clinical complexity in caring for homeless veterans (Weber et al., 2017; Tsai et al., 2019). We also hypothesized that RPIP would be most likely in homeless veterans, followed by veterans in supported housing, with IH veterans least likely to experience RPIP.
Methods
National VA data were extracted from the Corporate Data Warehouse, which contains the medical record, and the Homeless Operations Management Evaluation System (HOMES), which contains information on use of VA homeless services. Three groups of veterans with mental illness were identified:
Homeless veterans: using a VA operational definition (Tsai et al., 2022), this included anyone who had a) International Classification of Disease (ICD) 10 diagnostic code Z59.0 in inpatient and outpatient records OR b) clinical encounters with any of the following programs in 2021 or 2022: Grant and Per Diem program, Health Care for Homeless Veterans program, Domiciliary Care for Homeless Veterans, Compensated Work Therapy/Transitional Residence OR c) a positive screen on part 2 of an annual VA’s universal homeless screening clinical reminder. We removed anyone with a HUD-VASH encounter from this group to avoid duplicated cases.
Veterans in supported housing: a) anyone with a HUD-VASH encounter or HUD-VASH form in HOMES in 2021 or 2022, i.e. a history of homelessness, subsidized housing, and supportive case management services. We removed anyone who also met criteria for the homeless group over the study period to avoid duplicated cases.
IH veterans: a) anyone who screened negative on the homeless screening clinical reminder in 2021 or 2022 and b) had no indication of homelessness as outlined above.
Mental illness was defined as having a primary or secondary mental health diagnosis based on ICD-10 codes during 2021 or 2022. The index date was the first use of a VA homeless program in the homeless group, first interaction with HUD-VASH in the supported housing group, and the first visit for a mental health diagnosis in the IH group in 2021 or 2022. Veterans were considered to be prescribed stimulant and non-stimulant medications if they received at least 1 outpatient prescription fill, regardless of quantity, dosage form, or type of prescriber (e.g. primary care vs. psychiatric prescriber) during the study period.
Analysis
First, chi-square tests were used to compare sociodemographic, clinical, and military characteristics among homeless, HUD-VASH, and IH veterans. Analysis of variance was used to compare group differences in healthcare utilization within 1 year of the index date.
Second, chi-square tests were used to compare the frequency of prescriptions for amphetamines, methylphenidates, other stimulants typically used to treat narcolepsy, non-stimulants, and bupropion among homeless, HUD-VASH, and IH veterans.
Third, logistic regression was used to examine characteristics associated with any prescriptions (amphetamines, methylphenidates, other stimulants, and non-stimulants), any stimulant prescription, any amphetamine prescription, any methylphenidate prescriptions, other stimulant prescriptions, and non-stimulant prescriptions. We included in the logistic regression only variables that had at least one inter-group difference above 5% (Tsai et al., 2020).
Fourth, chi-square tests were conducted among homeless, HUD-VASH, and IH veterans with a prescription for any stimulant use to examine the extent to which RPIP was present. RPIP of stimulants was defined via literature and expert recommendations, including patients who have a stimulant use disorder due to risk of misuse; patients with psychotic disorders due to risk of worsening psychosis; as well as in people with certain medical conditions such as cardiovascular disease, hyperthyroidism, glaucoma, motor tics, agitated states, tourette’s disorder, and a concurrent MAO-I prescription, due to risk of medical adverse events (Corp et al., 2014; Tadrous et al., 2021).
These analyses, performed under the auspices of the national VA Homeless Programs Office using SAS/STAT version 9.2, were considered quality improvement and exempt from the VA institutional review board.
Results
Characteristics of the Population
The sample consisted of 105,062 homeless veterans, 33,884 veterans in HUD-VASH, and 1,875,083 IH veterans (Table 1). In addition to demographic differences, homeless veterans were more likely than IH and HUD-VASH veterans to be diagnosed with every mental illness. Homeless veterans were more likely to have the highest healthcare utilization among the 3 groups in every category, except for medical specialty visits.
Table 1.
Sociodemographic and clinical characteristics of homeless, HUD-VASH, and independently housed (IH) U.S. Department of Veterans Affairs (VA) service users with mental illness, 2021–2022.
| Variable | Homeless (N=105,062) | HUD-VASH (N=33,884) | IH (N=1,875,083) | Chi-square | p | HUD-VASH vs. Homeless | IH vs homeless | IH vs HUD-VASH | |||
|---|---|---|---|---|---|---|---|---|---|---|---|
| N | % | ||||||||||
| Age | 24313.59 | <.001 | |||||||||
| 17–29 | 7,369 | 7.0% | 897 | 2.7% | 94,053 | 5.0% | −4.4% | −2.0% | 2.4% | ||
| 30–39 | 22,108 | 21.0% | 4,522 | 13.4% | 297,402 | 15.9% | −7.7% | −5.2% | 2.5% | ||
| 40–49 | 17,022 | 16.2% | 4,475 | 13.2% | 281,281 | 15.0% | −3.0% | −1.2% | 1.8% | ||
| 50–59 | 19,945 | 19.0% | 6,937 | 20.5% | 320,659 | 17.1% | 1.5% | −1.9% | −3.4% | ||
| 60–69 | 25,129 | 23.9% | 12,435 | 36.7% | 331,114 | 17.7% | 12.8% | −6.3% | −19.0% | ||
| 70+ | 13,489 | 12.8% | 4,617 | 13.6% | 550,558 | 29.4% | 0.8% | 16.5% | 15.7% | ||
| Race/ethnicity | 19582.82 | <.001 | |||||||||
| Non-Hispanic white | 50,420 | 48.0% | 16,072 | 47.4% | 1,167,500 | 62.3% | −0.6% | 14.3% | 14.8% | ||
| Non-Hispanic black | 35,219 | 33.5% | 12,070 | 35.6% | 353,789 | 18.9% | 2.1% | −14.7% | −16.8% | ||
| Hispanic | 8,740 | 8.3% | 2,667 | 7.9% | 171,216 | 9.1% | −0.4% | 0.8% | 1.3% | ||
| Mixed race/other | 4,151 | 4.0% | 1,383 | 4.1% | 72,148 | 3.9% | 0.1% | −0.1% | −0.2% | ||
| Missing | 6,532 | 6.2% | 1,691 | 5.0% | 110,414 | 5.9% | −1.2% | −0.3% | 0.9% | ||
| Sex | 325.46 | <.001 | |||||||||
| Male | 88,511 | 84.3% | 28,105 | 83.0% | 1,604,906 | 85.6% | −1.3% | 1.3% | 2.6% | ||
| Female | 16,551 | 15.8% | 5,778 | 17.1% | 270,161 | 14.4% | 1.3% | −1.3% | −2.6% | ||
| Marital status | 47524.67 | <.001 | |||||||||
| Married | 28,432 | 27.1% | 7,021 | 20.7% | 1,033,337 | 55.1% | −6.3% | 28.1% | 34.4% | ||
| Single/never married | 29,826 | 28.4% | 10,237 | 30.2% | 316,483 | 16.9% | 1.8% | −11.5% | −13.3% | ||
| Divorced/separated | 44,107 | 42.0% | 15,591 | 46.0% | 473,679 | 25.3% | 4.0% | −16.7% | −20.8% | ||
| Widowed | 2,697 | 2.6% | 1,034 | 3.1% | 51,568 | 2.8% | 0.5% | 0.2% | −0.3% | ||
| Percent service-connected disability | 12176.89 | <.001 | |||||||||
| None/0% | 26,477 | 25.2% | 12,481 | 36.8% | 346,558 | 18.5% | 11.6% | −6.7% | −18.4% | ||
| 10–40% | 13,112 | 12.5% | 5,565 | 16.4% | 214,303 | 11.4% | 3.9% | −1.1% | −5.0% | ||
| 50–100% | 65,473 | 62.3% | 15,837 | 46.7% | 1,314,206 | 70.1% | −15.6% | 7.8% | 23.4% | ||
| Combat exposure | 13,894 | 13.2% | 3,272 | 9.7% | 291,742 | 15.6% | 1274.27 | <.001 | −3.6% | 2.3% | 5.9% |
| MST | 16,294 | 15.5% | 5,173 | 15.3% | 175,000 | 9.3% | 5499.37 | <.001 | −0.2% | −6.2% | −5.9% |
| Diagnosesa | |||||||||||
| Alcohol use disorder | 29,529 | 28.1% | 7,108 | 21.0% | 226,119 | 12.1% | 24494.09 | <.001 | −7.1% | −16.1% | −8.9% |
| Drug use disorder | 30,006 | 28.6% | 6,855 | 20.2% | 123,853 | 6.6% | 72352.51 | <.001 | −8.3% | −22.0% | −13.6% |
| Major depression | 48,188 | 45.9% | 13,285 | 39.2% | 702,414 | 37.5% | 3009.86 | <.001 | −6.7% | −8.4% | −1.8% |
| Bipolar spectrum disorder | 12,583 | 12.0% | 3,423 | 10.1% | 93,316 | 5.0% | 10962.65 | <.001 | −1.9% | −7.0% | −5.1% |
| Other mood disorder | 30,992 | 29.5% | 6,672 | 19.7% | 377,366 | 20.1% | 5360.41 | <.001 | −9.8% | −9.4% | 0.4% |
| Schizophrenia spectrum disorder | 8,269 | 7.9% | 2,217 | 6.5% | 48,642 | 2.6% | 11293.23 | <.001 | −1.3% | −5.3% | −4.0% |
| Other psychosis | 4,847 | 4.6% | 867 | 2.6% | 17,430 | 0.9% | 12488.77 | <.001 | −2.1% | −3.7% | −1.6% |
| GAD | 12,282 | 11.7% | 3,329 | 9.8% | 193,924 | 10.3% | 206.31 | <.001 | −1.9% | −1.4% | 0.5% |
| Anxiety-related disorder | 33,489 | 31.9% | 8,134 | 24.0% | 529,611 | 28.2% | 967.05 | <.001 | −7.9% | −3.6% | 4.2% |
| PTSD | 47,892 | 45.6% | 12,317 | 36.4% | 815,547 | 43.5% | 890.33 | <.001 | −9.2% | −2.1% | 7.1% |
| Other stress and adjustment disorder | 19,520 | 18.6% | 4,341 | 12.8% | 219,632 | 11.7% | 4429.86 | <.001 | −5.8% | −6.9% | −1.1% |
| Other MH | 23,351 | 22.2% | 5,716 | 16.9% | 299,376 | 16.0% | 2864.43 | <.001 | −5.4% | −6.3% | −0.9% |
| Self harm | 13,481 | 12.8% | 1,396 | 4.1% | 42,912 | 2.3% | 39871.99 | <.001 | −8.7% | −10.5% | −1.8% |
| Narcolepsy | 168 | 0.2% | 48 | 0.1% | 3,482 | 0.2% | 6.92 | .031 | 0.0% | 0.0% | 0.1% |
| ADHD | 5,393 | 5.1% | 1,172 | 3.5% | 79,712 | 4.3% | 245.57 | <.001 | −1.7% | −0.9% | 0.8% |
| Charlson Comorbidity Index | 387.12 | <.001 | |||||||||
| 0 | 67,867 | 64.6% | 20,919 | 61.7% | 1,163,650 | 62.1% | −2.9% | −2.5% | 0.3% | ||
| 1 | 16,703 | 15.9% | 6,036 | 17.8% | 330,958 | 17.7% | 1.9% | 1.8% | −0.2% | ||
| 2 | 6,994 | 6.7% | 2,536 | 7.5% | 141,556 | 7.6% | 0.8% | 0.9% | 0.1% | ||
| 3+ | 13,498 | 12.9% | 4,392 | 13.0% | 238,903 | 12.7% | 0.1% | −0.1% | −0.2% | ||
| −2.9% | −2.5% | ||||||||||
| Healthcare utilizationb | Mean | SD | Mean | SD | Mean | SD | F | p | |||
| Hospital admissions | |||||||||||
| Medical/surgical | 0.28 | 0.96 | 0.17 | 0.68 | 0.12 | 0.55 | 3802.77 | <.001 | −0.11 | −0.16 | −0.05 |
| Mental health | 0.17 | 0.67 | 0.04 | 0.27 | 0.02 | 0.16 | 24585.78 | <.001 | −0.13 | −0.15 | −0.02 |
| Substance use | 0.03 | 0.22 | 0.01 | 0.09 | 0.00 | 0.05 | 12494.35 | <.001 | −0.02 | −0.03 | −0.01 |
| Long-term care | 0.05 | 0.25 | 0.01 | 0.13 | 0.01 | 0.10 | 6511.05 | <.001 | −0.04 | −0.04 | 0.00 |
| Inpatient days | |||||||||||
| Medical/surgical | 1.72 | 11.46 | 0.75 | 4.60 | 0.51 | 4.51 | 2810.95 | <.001 | −1.21 | −0.24 | −1.21 |
| Mental health | 2.93 | 22.13 | 0.45 | 6.20 | 0.16 | 4.16 | 8999.96 | <.001 | −2.77 | −0.29 | −2.77 |
| Substance use | 1.21 | 9.60 | 0.21 | 3.43 | 0.04 | 1.47 | 9747.04 | <.001 | −1.17 | −0.17 | −1.17 |
| Long-term care | 3.55 | 34.14 | 0.70 | 14.74 | 0.51 | 16.52 | 1437.92 | <.001 | −3.04 | −0.19 | −3.04 |
| Emergency Dept/urgent care | 1.61 | 3.48 | 0.94 | 2.13 | 0.68 | 1.66 | 13341.09 | <.001 | −0.67 | −0.93 | −0.26 |
| Outpatient visits | |||||||||||
| Primary care | 12.24 | 16.96 | 11.30 | 12.90 | 10.84 | 11.79 | 686.14 | <.001 | −0.94 | −1.40 | −0.46 |
| Medical specialty | 8.39 | 16.36 | 8.63 | 17.31 | 7.99 | 14.21 | 68.51 | <.001 | 0.24 | −0.40 | −0.64 |
| Mental health | 28.04 | 56.14 | 13.76 | 30.58 | 7.91 | 16.88 | 46072.96 | <.001 | −14.28 | −20.13 | −5.85 |
| Substance abuse | 14.01 | 47.09 | 4.49 | 21.18 | 1.01 | 9.51 | 41209.27 | <.001 | −9.52 | −13.00 | −3.48 |
| Rehabilitation | 5.13 | 21.91 | 2.93 | 11.40 | 2.81 | 15.09 | 1120.61 | <.001 | −2.20 | −2.32 | −0.12 |
| Ancillary/diagnostic | 33.36 | 37.51 | 28.60 | 32.07 | 24.82 | 27.61 | 4756.41 | <.001 | −4.76 | −8.54 | −3.78 |
Participants could have more than one diagnosis.
The index date was the first use of a VA homeless program in the homeless group, first interaction with HUD-VASH in the supported housing group, and the first visit for a mental health diagnosis in the housed group in the 2021–2022 study period.
Stimulant and Non-Stimulant Prescribing
Amphetamine medications were the most commonly prescribed stimulant class, followed by methylphenidates (table 2). Of the non-stimulant medications, clonidine was most often prescribed. In unadjusted analyses, homeless veterans were more likely to be prescribed amphetamines overall relative to the IH group and HUD-VASH group (2.52% for homeless vs. 1.89% for HUD-VASH vs. 2.47% for IH), p < .001). However, homeless and HUD-VASH veterans were less likely to be prescribed methylphenidates than the IH group (0.91% for homeless vs. 0.76% for HUD-VASH vs. 1.01% for IH). Homeless veterans were more likely to be prescribed non-stimulant medications (atomoxetine, clonidine, guanfacine) relative to the HUD-VASH and IH groups (3.46% for homeless vs. 2.46% for HUD-VASH vs. 1.83% for IH, p <.001).
Table 2.
Frequency of stimulant prescriptions among homeless, HUD-VASH, and independently housed VA service users with mental illness, 2021–2022.
| Medication | Homeless (N=105,062) | HUD-VASH (N=33,884) | Independently Housed (N=1,875,083) | Chi-square | p | |||
|---|---|---|---|---|---|---|---|---|
| Amphetamines | 2,651 | 2.52% | 641 | 1.89% | 46,379 | 2.47% | 48.32 | <.001 |
| Amphetamine | 0 | 0.00% | 0 | 0.00% | 3 | 0.00% | 0.22 | .895 |
| Amphetamine resin complex | 1,527 | 1.45% | 335 | 0.99% | 26,102 | 1.39% | 42.97 | <.001 |
| Amphetamine/dextroamphetamine | 1,257 | 1.20% | 321 | 0.95% | 22,018 | 1.17% | 15.39 | <.001 |
| Dextroaphmetamine | 236 | 0.22% | 81 | 0.24% | 4,618 | 0.25% | 1.96 | 0.376 |
| Lisdexamfetamine | 317 | 0.30% | 69 | 0.20% | 4,816 | 0.26% | 11.78 | 0.003 |
| Methylphenidates | 955 | 0.91% | 261 | 0.77% | 19,020 | 1.01% | 30.16 | <.001 |
| Dexmethylphenidate | 3 | 0.00% | 4 | 0.01% | 50 | 0.00% | 9.82 | 0.007 |
| Methylphenidate | 953 | 0.91% | 258 | 0.76% | 18,985 | 1.01% | 31.36 | <.001 |
| Other stimulants | 454 | 0.43% | 153 | 0.45% | 9,972 | 0.53% | 22.51 | <.001 |
| Armodafinil | 134 | 0.13% | 46 | 0.14% | 2,976 | 0.16% | 7.14 | 0.028 |
| Modafinil | 347 | 0.33% | 117 | 0.35% | 7,550 | 0.40% | 15.55 | <.001 |
| Non-stimulants | 3,638 | 3.46% | 834 | 2.46% | 34,399 | 1.83% | 1444.82 | <.001 |
| Atomoxetine | 925 | 0.88% | 161 | 0.48% | 7,727 | 0.41% | 502.01 | <.001 |
| Clonidine | 2,656 | 2.53% | 662 | 1.95% | 26,011 | 1.39% | 961.75 | <.001 |
| Guanfacine | 159 | 0.15% | 29 | 0.09% | 1,243 | 0.07% | 102.38 | <.001 |
| Bupropion | 12,726 | 12.11% | 3,696 | 10.91% | 232,707 | 12.41% | 76.11 | <.001 |
| Any (excluding bupropion) | 7,047 | 6.71% | 1,758 | 5.19% | 101,662 | 5.42% | 323.10 | <.001 |
| Any (including bupropion) | 18,186 | 17.31% | 5,081 | 15.00% | 312,360 | 16.66% | 99.53 | <.001 |
Based on logistic regression, adjusted for sociodemographic, clinical, and health care utilization characteristics, there was no significant difference in likelihood of homeless veterans being prescribed any overall medication (stimulant or non-stimulant) relative to IH veterans (Table 3) (aOR = 0.97, CI = 0.92–1.01). However, when looking at stimulants specifically, homeless veterans were less likely to be prescribed any stimulant overall relative to IH veterans (aOR = 0.80, CI = 0.76–0.85), including amphetamines, methylphenidates and other stimulant medications. Homeless veterans were also more likely to be prescribed non-stimulant medications (aOR = 1.12, CI = 1.06–1.18). For HUD-VASH veterans, there was no significant difference in likelihood of prescribed stimulants (aOR = .90, CI = 0.80–1.01), but HUD-VASH veterans more likely to be prescribed non-stimulants (aOR = 1.12, CI = 1.02–1.23) relative to the IH group.
Table 3.
Characteristics associated with receiving or not receiving a stimulant or non-stimulant prescription, 2021–2022.
| Variable | Any | Any stimulant | Amphetamines | Methylphenidates | Other stimulants | Non-stimulants | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| OR | 99% CI | p | OR | 99% CI | p | OR | 99% CI | p | OR | 99% CI | p | OR | 99% CI | p | OR | 99% CI | p | |
| Housing status (ref independently housed) | ||||||||||||||||||
| HUD-VASH | 1.03 | (0.95–1.12) | .419 | 0.90 | (0.80–1.01) | .061 | 0.93 | (0.82–1.06) | .294 | 0.87 | (0.73–1.03) | .109 | 1.05 | (0.84–1.30) | .676 | 1.12 | (1.02–1.23) | .017 |
| Homeless | 0.97 | (0.92–1.01) | .125 | 0.80 | (0.76–0.85) | <.001 | 0.89 | (0.83–0.95) | <.001 | 0.80 | (0.73–0.88) | <.001 | 0.85 | (0.74–0.97) | .018 | 1.12 | (1.06–1.18) | <.001 |
| Age (ref 60–69) | ||||||||||||||||||
| 17–29 | 1.15 | (1.09–1.21) | <.001 | 1.56 | (1.46–1.66) | <.001 | 2.64 | (2.44–2.85) | <.001 | 0.85 | (0.77–0.94) | .001 | 0.65 | (0.56–0.76) | <.001 | 0.93 | (0.87–1.00) | .041 |
| 30–39 | 1.46 | (1.41–1.52) | <.001 | 2.03 | (1.93–2.14) | <.001 | 3.07 | (2.88–3.27) | <.001 | 0.91 | (0.85–0.98) | .019 | 1.12 | (1.02–1.24) | .017 | 1.01 | (0.96–1.06) | .793 |
| 40–49 | 1.40 | (1.35–1.45) | <.001 | 1.90 | (1.81–2.00) | <.001 | 2.50 | (2.34–2.67) | <.001 | 1.07 | (1.00–1.16) | .066 | 1.45 | (1.32–1.59) | <.001 | 1.03 | (0.99–1.09) | .161 |
| 50–59 | 1.22 | (1.18–1.26) | <.001 | 1.51 | (1.43–1.58) | <.001 | 1.73 | (1.62–1.86) | <.001 | 1.10 | (1.02–1.19) | .018 | 1.40 | (1.28–1.53) | <.001 | 1.02 | (0.98–1.07) | .348 |
| 70+ | 0.73 | (0.70–0.76) | <.001 | 0.64 | (0.60–0.67) | <.001 | 0.50 | (0.46–0.55) | <.001 | 0.83 | (0.76–0.90) | <.001 | 0.61 | (0.56–0.67) | <.001 | 0.85 | (0.81–0.89) | <.001 |
| Race/ethnicity (ref non-Hispanic white) | ||||||||||||||||||
| Non-Hispanic black | 0.82 | (0.79–0.84) | <.001 | 0.55 | (0.53–0.58) | <.001 | 0.64 | (0.60–0.67) | <.001 | 0.61 | (0.57–0.66) | <.001 | 0.53 | (0.49–0.58) | <.001 | 1.10 | (1.07–1.14) | <.001 |
| Hispanic | 0.77 | (0.75–0.80) | <.001 | 0.76 | (0.72–0.79) | <.001 | 0.84 | (0.80–0.89) | <.001 | 0.89 | (0.83–0.96) | .001 | 0.54 | (0.48–0.60) | <.001 | 0.83 | (0.79–0.88) | <.001 |
| Mixed race/other | 0.92 | (0.87–0.97) | .001 | 0.91 | (0.85–0.97) | .002 | 0.95 | (0.88–1.02) | .152 | 0.97 | (0.88–1.07) | .592 | 0.80 | (0.70–0.91) | <.001 | 0.93 | (0.86–1.00) | .037 |
| Missing | 0.88 | (0.85–0.92) | <.001 | 0.82 | (0.78–0.87) | <.001 | 0.90 | (0.84–0.96) | <.001 | 0.85 | (0.78–0.93) | <.001 | 0.80 | (0.71–0.90) | <.001 | 1.00 | (0.94–1.06) | .910 |
| Marital status (ref married) | ||||||||||||||||||
| Single/never married | 1.01 | (0.98–1.04) | .436 | 1.04 | (1.01–1.08) | .018 | 1.09 | (1.04–1.13) | <.001 | 1.05 | (1.00–1.10) | .074 | 0.85 | (0.78–0.91) | <.001 | 0.97 | (0.93–1.01) | .135 |
| Divorced/separated | 1.02 | (1.00–1.05) | .101 | 1.01 | (0.97–1.04) | .707 | 1.13 | (1.09–1.18) | <.001 | 0.96 | (0.91–1.01) | .092 | 0.77 | (0.72–0.82) | <.001 | 1.03 | (1.00–1.06) | .072 |
| Widowed | 0.92 | (0.85–1.00) | .049 | 0.75 | (0.66–0.85) | <.001 | 0.95 | (0.80–1.14) | .581 | 0.69 | (0.56–0.85) | <.001 | 0.63 | (0.50–0.79) | <.001 | 1.04 | (0.95–1.15) | .374 |
| Percent service-connected (ref none/0%) | ||||||||||||||||||
| 10–40% | 0.99 | (0.95–1.03) | .675 | 1.04 | (0.98–1.09) | .168 | 1.02 | (0.96–1.08) | .606 | 0.99 | (0.92–1.07) | .839 | 1.14 | (1.00–1.29) | .046 | 0.94 | (0.89–1.00) | .034 |
| 50–100% | 1.23 | (1.19–1.27) | <.001 | 1.30 | (1.24–1.35) | <.001 | 1.15 | (1.09–1.21) | <.001 | 1.10 | (1.03–1.17) | .003 | 1.92 | (1.74–2.11) | <.001 | 1.12 | (1.08–1.17) | <.001 |
| Combat exposure | 1.02 | (0.99–1.05) | .154 | 1.03 | (0.99–1.07) | .093 | 1.00 | (0.96–1.05) | .858 | 1.05 | (0.99–1.11) | .098 | 1.01 | (0.94–1.09) | .791 | 0.99 | (0.95–1.03) | .600 |
| Military sexual trauma | 1.04 | (1.01–1.08) | .009 | 1.04 | (1.00–1.09) | .034 | 1.06 | (1.01–1.11) | .017 | 1.04 | (0.98–1.11) | .165 | 0.96 | (0.89–1.04) | .360 | 1.02 | (0.98–1.06) | .324 |
| Diagnosesa | ||||||||||||||||||
| Alcohol use disorder | 0.79 | (0.77–0.82) | <.001 | 0.61 | (0.58–0.63) | <.001 | 0.69 | (0.66–0.73) | <.001 | 0.69 | (0.64–0.74) | <.001 | 0.58 | (0.53–0.65) | <.001 | 1.05 | (1.01–1.10) | .010 |
| Drug use disorder | 1.19 | (1.15–1.24) | <.001 | 0.64 | (0.61–0.67) | <.001 | 0.70 | (0.66–0.74) | <.001 | 0.72 | (0.67–0.78) | <.001 | 0.80 | (0.72–0.90) | <.001 | 1.90 | (1.82–1.97) | <.001 |
| Major depression | 1.21 | (1.19–1.24) | <.001 | 1.22 | (1.19–1.26) | <.001 | 1.02 | (0.98–1.05) | .311 | 1.26 | (1.21–1.31) | <.001 | 1.71 | (1.62–1.81) | <.001 | 1.18 | (1.15–1.22) | <.001 |
| Bipolar spectrum disorder | 1.09 | (1.05–1.14) | <.001 | 0.93 | (0.89–0.98) | .008 | 0.84 | (0.80–0.89) | <.001 | 1.04 | (0.96–1.12) | .321 | 1.40 | (1.27–1.55) | <.001 | 1.31 | (1.25–1.38) | <.001 |
| Other mood disorder | 1.03 | (1.00–1.06) | .018 | 1.03 | (1.00–1.06) | .092 | 0.90 | (0.87–0.94) | <.001 | 1.11 | (1.06–1.16) | <.001 | 1.38 | (1.30–1.46) | <.001 | 1.05 | (1.02–1.08) | .003 |
| Schizophrenia spectrum disorder | 0.65 | (0.61–0.70) | <.001 | 0.40 | (0.35–0.45) | <.001 | 0.38 | (0.33–0.45) | <.001 | 0.57 | (0.47–0.69) | <.001 | 0.63 | (0.51–0.78) | <.001 | 0.96 | (0.89–1.05) | .369 |
| Anxiety-related disorder | 0.97 | (0.95–0.99) | .007 | 0.82 | (0.80–0.85) | <.001 | 0.84 | (0.81–0.86) | <.001 | 0.94 | (0.90–0.98) | .002 | 0.95 | (0.90–1.01) | .098 | 1.22 | (1.19–1.26) | <.001 |
| Post-traumatic stress disorder | 1.21 | (1.18–1.24) | <.001 | 1.07 | (1.04–1.10) | <.001 | 1.08 | (1.04–1.11) | <.001 | 1.06 | (1.01–1.10) | .015 | 0.98 | (0.92–1.04) | .469 | 1.37 | (1.33–1.41) | <.001 |
| Non-PTSD stress disorder | 0.84 | (0.81–0.86) | <.001 | 0.80 | (0.77–0.83) | <.001 | 0.81 | (0.78–0.85) | <.001 | 0.89 | (0.84–0.94) | <.001 | 0.93 | (0.86–1.00) | .060 | 0.94 | (0.90–0.98) | .001 |
| Other mental health disorder | 1.35 | (1.31–1.39) | <.001 | 1.36 | (1.29–1.42) | <.001 | 1.54 | (1.43–1.65) | <.001 | 1.24 | (1.13–1.36) | <.001 | 1.28 | (1.19–1.38) | <.001 | 1.31 | (1.26–1.37) | <.001 |
| Suicidality/self-harm | 0.78 | (0.73–0.82) | <.001 | 0.61 | (0.56–0.66) | <.001 | 0.65 | (0.59–0.71) | <.001 | 0.72 | (0.64–0.81) | <.001 | 0.72 | (0.61–0.84) | <.001 | 1.00 | (0.94–1.07) | .969 |
| Narcolepsy | 47.15 | (42.97–51.74) | <.001 | 110.94 | (100.83–122.08) | <.001 | 17.68 | (15.20–20.57) | <.001 | 16.60 | (13.91–19.81) | <.001 | 133.33 | (121.25–146.62) | <.001 | 0.98 | (0.75–1.30) | .911 |
| Attention deficit hyperactivity disorder | 54.77 | (52.87–56.74) | <.001 | 90.82 | (86.55–95.30) | <.001 | 95.32 | (89.07–102.02) | <.001 | 54.03 | (49.27–59.25) | <.001 | 1.56 | (1.40–1.74) | <.001 | 5.54 | (5.28–5.81) | <.001 |
| Hospital daysb | ||||||||||||||||||
| Medical/surgical | 0.98 | (0.98–0.98) | <.001 | 0.96 | (0.95–0.96) | <.001 | 0.95 | (0.94–0.96) | <.001 | 0.97 | (0.96–0.98) | <.001 | 0.97 | (0.96–0.98) | <.001 | 0.99 | (0.98–0.99) | <.001 |
| Mental health | 0.99 | (0.99–0.99) | <.001 | 0.98 | (0.98–0.99) | <.001 | 0.98 | (0.98–0.99) | <.001 | 0.99 | (0.98–0.99) | <.001 | 0.99 | (0.98–1.00) | .008 | 1.00 | (0.99–1.00) | <.001 |
| Substance use | 0.99 | (0.99–0.99) | <.001 | 0.98 | (0.97–0.98) | <.001 | 0.98 | (0.97–0.99) | <.001 | 0.98 | (0.97–0.99) | .003 | 0.99 | (0.97–1.00) | .079 | 1.00 | (0.99–1.00) | .004 |
| Long-term care | 1.00 | (0.99–1.00) | <.001 | 0.99 | (0.99–1.00) | <.001 | 0.99 | (0.99–1.00) | .001 | 0.99 | (0.99–1.00) | .010 | 0.99 | (0.99–1.00) | .006 | 1.00 | (1.00–1.00) | <.001 |
| Outpatient visits | ||||||||||||||||||
| Mental health | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.00–1.01) | <.001 | 1.00 | (1.00–1.00) | <.001 |
| Substance use | 1.00 | (1.00–1.00) | <.001 | 0.99 | (0.99–0.99) | <.001 | 0.99 | (0.99–0.99) | <.001 | 0.99 | (0.99–1.00) | <.001 | 1.00 | (1.00–1.00) | .018 | 1.00 | (1.00–1.00) | <.001 |
| Rehabilitation | 1.00 | (1.00–1.00) | .106 | 1.00 | (1.00–1.00) | .011 | 1.00 | (1.00–1.00) | .003 | 1.00 | (1.00–1.00) | .040 | 1.00 | (1.00–1.00) | <.001 | 1.00 | (1.00–1.00) | <.001 |
| Ancillary/diagnostic | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.00 | (1.00–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 | 1.01 | (1.01–1.01) | <.001 |
The reference is the absence of a given diagnosis.
The reference is the absence of a particular type of visit or admission.
Risky and Potentially Inappropriate Prescribing
As shown in Table 4, homeless veterans exhibited the highest rate of stimulants prescribed in the presence of a psychotic disorder (7.9% vs. 6.4% for HUD-VASH vs. 2.2% for IH, p <.001), cocaine use disorder (11.8% vs. 6.1% for HUD-VASH vs. 1.0% for IH, p < .001), stimulant disorder (13.4% vs. 6.0% for HUD-VASH vs. 1.3% for IH, p < .001), and agitated states (0.7% vs. 0.1% for HUD-VASH vs. 0.1% for IH, p < .001). There was no difference in rates of stimulant prescribing for other categories of RPIP.
Table 4.
Risky and potentially inappropriate prescribing of stimulants among homeless, HUD-VASH, and independently housed VA service users with mental illness and contraindicating or complicating factors, 2021–2022.
| Contraindication/complicating factor | Homeless (N=7,047) | HUD-VASH (N=1,758) | Independently Housed (N=101,662) | Chi-square | p | |||
|---|---|---|---|---|---|---|---|---|
| N | % | N | % | N | % | |||
| Psychotic disorder | 554 | 7.9% | 112 | 6.4% | 2,182 | 2.2% | 959.335 | <.001 |
| Cocaine use disorder | 828 | 11.8% | 108 | 6.1% | 980 | 1.0% | 4701.852 | <.001 |
| Other stimulant use disorder | 943 | 13.4% | 106 | 6.0% | 1,304 | 1.3% | 4757.850 | <.001 |
| Hyperthyroidism | 16 | 0.2% | 4 | 0.2% | 980 | 1.0% | 0.936 | .626 |
| Glaucoma | 115 | 1.6% | 35 | 2.0% | 1,304 | 0.2% | 3.307 | .191 |
| Cardiac condition | 2,019 | 28.7% | 513 | 29.2% | 184 | 0.2% | 0.926 | .629 |
| Tourette’s/other tic disorder | 15 | .2% | 2 | .1% | 286 | .3% | 2.812 | .245 |
| Agitated state | 52 | .7% | 2 | .1% | 97 | .1% | 199.355 | <.001 |
| Concurrent MAOI | 11 | .2% | 1 | .1% | 91 | .1% | 3.390 | .184 |
Discussion
To our knowledge, this is the first national study to examine stimulant and non-stimulant ADHD medication prescriptions in a US homeless population. We found in adjusted analyses that overall, homeless veterans were less likely to be prescribed stimulants and more likely to be prescribed non-stimulant ADHD medications relative to IH veterans with mental illness. However, we also found a higher rate of RPIP of prescribing stimulants in homeless and HUD-VASH veterans compared to IH veterans.
The finding that homeless veterans were not prescribed stimulants more frequently relative to the IH and HUD-VASH groups was contrary to our first hypothesis. This finding cannot be easily explained away by differences in diagnoses of ADHD or healthcare utilization since they were higher among homeless veterans. Other explanations may therefore explain these results. First, because homeless patients within the VA system may have better access to outpatient mental health services relative to those outside the VA system as shown by the higher number of outpatient mental health visits, their mental health may be better managed. Patients therefore may not seek stimulants as much as would be if their mental health were more acute with less access to outpatient care. Second, it is possible that VA providers, who operate in a national healthcare system with academic detailing initiatives that provide targeted educational outreach, may be more hesitant to prescribe stimulants to homeless relative to housed individuals due to perceived risks or stereotypes that they will misuse or divert the stimulant—supported by the finding that non-stimulant medications were more likely to be prescribed for the homeless group. Third, as many homeless veterans manage a range of medical and psychiatric comorbidities, it is possible that ADHD went undiagnosed in the face of competing clinical and social priorities. Further study is required to determine which of these explanations is most plausible.
Our second study hypothesis was supported as we found RPIP of stimulants was most likely in homeless veterans, followed by HUD-VASH veterans, with IH veterans least likely to experience RPIP. The high rate of RPIP in the context of a psychotic disorder (7.9%) among homeless patients is especially clinically noteworthy. Clinical practice guidelines caution against stimulant use among veterans with psychotic disorders, given risk of exacerbating psychosis (Moran et al., 2024), which in the homeless population heightens their risk of remaining homeless. Non-stimulant medications can be considered as alternative treatments in this context. It therefore seems that although homeless veterans are less likely to be prescribed stimulants, they are more likely to be prescribed them under RPIP circumstances—perhaps supporting reasons that prescribers are less likely to prescribe stimulants to homeless veterans. That HUD-VASH veterans were also more likely to receive RPIP prescriptions relative to the IH group suggests that this group is viewed by clinicians differently than both the homeless and IH group, with more prudent prescribing for this population needed.
It is worth noting that stimulants were prescribed for homeless veterans in high rates in the presence of a cocaine use disorder (11.8%) and stimulant use disorder (13.4%). Though this may qualify as inappropriate prescribing given risks of combining stimulants with ongoing illicit stimulant use, the higher prevalence could also partly reflect an informed approach to try to treat an underlying stimulant use disorder. Although availability of pharmacotherapies for stimulant use is more limited than treatment for opioid addiction, recent evidence has suggested that prescribed psychostimulants can support the management of cravings and withdrawal symptoms in patients with stimulant use disorder and reduce illicit stimulant use (Tardelli et al., 2020). This approach has been increasing in recent years and may be considered. However, prescribing stimulants for stimulant use disorder must also be balanced with the health risks of excess stimulant use in the case of continued illicit use. Clinicians should prioritize evidence-based psychosocial interventions for stimulant use disorders, including contingency management and cognitive behavioral therapy (Minozzi et al., 2024).
Limitations
This study has several limitations. First, we could not tell the diagnoses for which these scripts were specifically prescribed, thus it is not clear to what extent these medications were prescribed specifically for ADHD vs another indication such as stimulant use disorder. Second, the data do not measure the total dose prescribed. Third, the observed higher RPIP of stimulants found in homeless veterans may partly reflect their poorer mental and physical health relative to their housed counterparts since they were more likely to have contraindicating/complicating factors. Our analysis of RPIP was not focused on adjusting for these factors but rather to describe the extent of RPIP as is applicable to real-world practice. Fourth, homeless veterans may have had additional prescription medication refills provided through non-VA pharmacies that could not be captured in the VA medical record. Future research could compare stimulant prescribing in a non-veteran homeless and housed population to determine to what extent these findings are generalizable outside of the VA.
Conclusion
This study provides a novel evaluation of stimulant prescribing in a national sample of homeless veterans, a population at high risk for psychiatric disorders, substance use disorders, medical illness, and death. This study motivates the need for heightened attention and further examination of safe prescribing of stimulants for homeless veterans and those in supported housing.
Footnotes
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Declaration of Interest
Katherine Koh: I have nothing to declare.
Dorota Szymkowiak: I have nothing to declare.
Jack Tsai: I have nothing to declare.
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