Abstract
Smoking is highly prevalent among people living with HIV (PLWH) and increases cardiovascular risk. Pharmacotherapies such as nicotine replacement therapy (NRT), bupropion, and varenicline help to reduce smoking, though rates of receipt among PLWH compared with HIV-uninfected persons are unknown. Among 814 PLWH and 908 uninfected patients enrolled in the Veterans Aging Cohort Study (2012–2017) who reported current smoking, we used marginal multivariable log-linear regression models to estimate adjusted relative risks (ARR) of receiving pharmacotherapy by HIV status. We also assessed patient-level factors associated with pharmacotherapy receipt within each group. In multivariable analyses, receipt of NRT was less likely among PLWH relative to uninfected participants (ARR 0.77, 95% CI 0.67, 0.89). In both populations, documented mental health disorders and contemplation to quit were associated with greater likelihood of receiving pharmacotherapy. Further research is needed to explore potential treatment disparities.
Keywords: smoking, smoking cessation, smoking cessation pharmacotherapy, HIV, Veterans
Introduction
Since HIV has become a manageable chronic disease due to effective antiretroviral therapy (ART) (1,2), people living with HIV (PLWH) are surviving albeit with an increased burden of non-infectious, non-AIDS-related illnesses, such as cardiovascular disease (CVD) (3,4). Smoking and HIV are independent risk factors for CVD, and the health consequences of CVD are compounded for PLWH who smoke (4–6). Thus, smoking cessation has emerged as a critical, modifiable component to improve the health of PLWH. The Food and Drug Administration (FDA) has approved effective pharmacologic treatments for tobacco use disorder, namely nicotine replacement therapy (NRT), bupropion, and varenicline. These medications have been shown to be effective in improving quit rates in populations with and without HIV (7–13).
PLWH and HIV-uninfected persons may have differing access to smoking cessation pharmacotherapies as a result of factors across multiple domains (e.g., factors related to comorbid conditions, smoking-related behaviors and attitudes, and issues specific to the experience of living with HIV and receiving regular care for HIV). For example, PLWH may have limited access to smoking cessation pharmacotherapy relative to uninfected populations due to their comorbid, complex care needs. Comorbid substance use and mental health disorders (e.g., unhealthy alcohol use and depression) are separately and independently associated with tobacco use among PLWH and may represent major barriers to smoking cessation (14–16). Management of these conditions in combination with HIV, may present logistical (e.g, time) or training (e.g., content expertise) barriers to addressing smoking in HIV care settings (17–19). On the other hand, PLWH may have greater health care utilization than uninfected persons as a result of receiving regular HIV care consistent with clinical guidelines to ensure immune competence and HIV viral suppression (20). As a result, PLWH may be in more frequent contact with a health care provider and have more opportunities to receive smoking cessation medications.
Despite the importance of promoting smoking cessation among PLWH, it is unclear how frequently PLWH who smoke are receiving pharmacotherapies. Therefore, in a national sample of patients receiving care in the Veterans Health Administration (VHA) recruited to the Veterans Aging Cohort Study (VACS), we determined receipt of smoking cessation pharmacotherapy among PLWH, overall and compared with a matched sample of uninfected patients. We also evaluated patient-level factors associated with receipt of pharmacotherapy among PLWH and HIV-uninfected participants separately.
Methods
Study Data Sources and Sample
VACS is a multi-site, longitudinal, prospective cohort study of PLWH and HIV-uninfected controls receiving care in nine VHA Infectious Disease (ID) and General Medicine Clinics, respectively (21). Specifically, uninfected participants are group matched by five-year blocks of age, race/ethnicity, sex, and site of care to PLWH from VHA facilities in Atlanta, Baltimore, the Bronx, Dallas, Houston, Los Angeles, Manhattan/Brooklyn, Pittsburgh, and Washington D.C. For the present study, we included the 1722 patients (814 PLWH and 908 uninfected) who were surveyed between 2012 and 2017 who reported “still smoking” or daily smoking.
VACS survey data were linked to electronic health record (EHR) data extracted from the VHA Corporate Data Warehouse (CDW), which includes clinical, administrative, pharmacy, and utilization data for all users of VHA care (22).
Primary Independent Measure
HIV status was defined as having two outpatient or one inpatient diagnostic codes from the International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) for either AIDS (042) or HIV (V08), consistent with prior research conducted in VACS (23).
Outcomes
The primary outcome of interest was a dichotomous measure identifying receipt of any smoking cessation pharmacotherapy (NRT, bupropion, and varencline), based on outpatient prescriptions filled from VHA pharmacy data in the CDW 0–365 days following the date of completed survey. Secondary outcomes were derived to measure receipt of each specific medication 0–365 days following the date of completed survey.
Covariates
Covariate selection was guided by a conceptual model that includes factors hypothesized to impact the likelihood of receiving smoking cessation pharmacotherapy. These include sociodemographic factors, substance use and mental health comorbidity, smoking-related comorbid conditions, HIV severity, health care utilization, and smoking-related attitudes and behaviors.
Sociodemographic factors such as age at time of survey (continuous), sex (male versus female), and race/ethnicity (black, Hispanic, white, other) were asked on survey and available in administrative data sources. “Other” race included American Indian or Alaska Native, Asian, and Native Hawaiian or Pacific Islander. Education (less than high school, high school graduate or GED, some college or technical school, college graduate or graduate school), and annual income (earning less than $12,000, $12,000–24,999, $25,000 or greater) were assessed in individual survey items.
Substance use and mental health comorbidity were ascertained through both survey items and the EHR. In the survey, alcohol use was evaluated using the three-item Alcohol Use Disorders Identification Test Consumption (AUDIT-C) questionnaire, which is a brief alcohol screen validated to identify patients with unhealthy alcohol use (24). Men who scored 4 or greater and women who scored 3 or greater were identified as having unhealthy alcohol use (25). Surveyed participants were also asked to identify the frequency with which they had used cocaine or crack, amphetamines, and/or heroin in the last 12 months. We coded a dichotomous “any substance use in the last year” measure as those participants who reported “less than once per month” or more frequent use of any of these drugs compared with those reporting “no use in the last year” or “have never tried” for all drugs. Depression was assessed via survey using the Patient Health Questionnaire (PHQ-9), a 9-item screen for which a value greater than nine has an 88% sensitivity and 88% specificity for diagnosing moderate to severe depression (26). ICD-9 codes pertaining to other psychiatric conditions (bipolar disorder, major/minor depression, post-traumatic stress disorder, schizophrenia, and other psychosis) were grouped together (27), and were defined as having a recent psychiatric diagnosis if a code occurred 0–365 days prior to completing survey.
Smoking-related comorbid conditions were ascertained using diagnostic coding in the EHR, and included CVD (ICD-9 CM codes for acute myocardial infarction, coronary artery disease, congestive heart failure, hypertension, stroke, other CVD); chronic pulmonary disease (ICD-9 CM codes for COPD, obstructive lung disease, pulmonary hypertension, other pulmonary disorder); and non-AIDS malignancies (e.g., Hodgkin lymphoma and cancers of the mouth, throat, liver, lung, and anus) (28) documented 0–365 days prior to survey date.
HIV severity was measured using the VACS Index 2.0, a composite measure of HIV severity that predicts all-cause mortality, cause-specific mortality, frailty, and other clinical outcomes in people with HIV (29) as well as in persons without HIV infection (30,31). VACS Index 2.0 includes CD4+ cell count and HIV-1 RNA in combination with other disease markers (32–34), and is generated using a combination of administrative, clinical, and laboratory data. Individual components of VACS 2.0 documented in the CDW closest to date of survey was used to calculate the continuous summary score.
Health care utilization included outpatient and inpatient visits captured using visit and stop codes documented in the CDW 0–365 days prior to survey date. The number of outpatient visits was limited to primary care encounters. These were summed and then categorized into 1–2, 3–5, and 6 visits or greater based on 1–106 visits distribution (35). Inpatient visits were summed and categorized into 0, 1, and 2 visits or greater based on a 0–16 visits distribution.
Smoking-related attitudes and behaviors were ascertained via survey questions. One survey item asked whether respondents were currently thinking about quitting. We coded a dichotomous “contemplation to quit” measure as those thinking about quitting, while grouping those who selected that they were “not sure” or “not thinking about quitting” or “not ready to quit” as not contemplating cessation. Respondents who reported at least one quit attempt were coded as having a past quit attempt. The average number of cigarettes smoked per day was also assessed on the survey and measured in our study as less than 10, 10–20, and greater than 20 cigarettes.
Analysis
We described the demographic and clinical characteristics of the sample, overall and across HIV status using chi-square tests of independence, Student’s t-test, and Wilcoxon Rank-Sum test as appropriate. We also described the rate of primary and secondary pharmacotherapy outcomes in the overall sample.
To assess receipt of pharmacotherapy among PLWH overall, and relative to HIV-uninfected persons, we first conducted bivariate comparisons of the prevalence of primary and secondary outcomes (across HIV status using chi-square tests). Subsequently, we fit marginal multivariable log-linear regression models with an over-dispersed Poisson working model to estimate the adjusted relative risk (ARR) and 95% confidence interval (CI) of pharmacotherapy receipt for PLWH compared with uninfected participants. Models were adjusted for all measured covariates in the domains described above. Logarithmic, rather than logistic link function, was used because the outcome was expected to be somewhat common, in which case odds ratios may overestimate the relative risks (36,37). In order to account for the multi-level nature of the data, we clustered by site of care and specified an exchangeable correlation structure. Further, we used a robust sandwich estimator to calculate standard errors and obtain a valid inference even if the correlation structure was incorrectly specified (35–39).
The same model was repeated stratified by HIV status so that we could compare which patient-level factors were associated with pharmacotherapy receipt in populations with and without HIV. All analyses were performed using Stata version 14 (Stata Corp, College Station, TX).
Results
Study Population
Among 4113 VACS participants, 1722 (42%) self-reported current smoking (n=814 of 2163 PLWH and 908 of 1950 HIV-uninfected) and were thus eligible for the present study. Participants in our sample were predominantly black (74%), male (96%), had a mean age of 56 years, and at least some college or technical school education (89%) (Table 1). Most PLWH were on ART, had well-controlled HIV with suppressed HIV viral load, and a median CD4+ count of 483 cells/m3.
Table 1:
Characteristics of Patients Reporting Current Smoking who Completed VACS Survey between 2012 and 2017, Overall and by HIV Status
| Study Population (n=1722) |
HIV+ (n=814) |
HIV− (n=908) |
p value | |
|---|---|---|---|---|
| SOCIODEMOGRAPHIC FACTORS | ||||
| Age, mean (SD) | 56.2 (8.3) | 56.4 (8.1) | 56.1 (8.5) | NS |
| Male, n (%) | 1646 (96) | 786 (97) | 860 (95) | NS |
| Race/ethnicity, n (%) | 0.01 | |||
| Black | 1280 (74) | 623 (77) | 657 (72) | |
| Hispanic | 93 (5) | 49 (6) | 44 (5) | |
| Other | 89 (5) | 42 (5) | 47 (5) | |
| White | 257 (15) | 98 (12) | 159 (18) | |
| Education, n (%) | NS | |||
| < High school | 104 (6) | 49 (6) | 55 (6) | |
| High school grad, GED | 647 (38) | 290 (36) | 357 (39) | |
| Some college or technical school | 779 (45) | 374 (46) | 405 (45) | |
| College grad, higher education | 184 (11) | 97 (12) | 87 (10) | |
| Married or living with partner, n (%) | 374 (22) | 169 (21) | 205 (23) | NS |
| Annual income, n (%) | 0.01 | |||
| <$1200 | 741 (44) | 330 (42) | 411 (47) | |
| $1200–24999 | 485 (29) | 259 (33) | 226 (26) | |
| ≥ $25000 | 443 (27) | 200 (25) | 243 (28) | |
| SUBSTANCE USE / MENTAL HEALTH COMORBIDITY | ||||
| Audit-C score, median (IQR) | 2 (0–5) | 2 (0–4) | 2 (0–5) | 0.05 |
| Unhealthy alcohol use, n (%) | 595 (35) | 256 (31) | 339 (37) | 0.01 |
| Substance use in the last year, n (%) | ||||
| Marijuana | 557 (33) | 288 (36) | 269 (30) | 0.01 |
| Cocaine or crack | 447 (26) | 202 (25) | 245 (27) | NS |
| Amphetamines | 108 (6) | 51 (6) | 57 (6) | NS |
| Heroin | 128 (8) | 57 (7) | 71 (8) | NS |
| Prescription opioids | 331 (19) | 155 (19) | 176 (20) | NS |
| Depression severity (PHQ-9), median (IQR) | 4 (1–9) | 4 (1–9) | 5 (1–10) | 0.01 |
| Moderate to severe depression, n (%) | 407 (24) | 171 (21) | 236 (26) | 0.02 |
| Psychiatric disorders, n (%) | 781 (48) | 296 (38) | 485 (57) | <0.001 |
| SMOKING-RELATED COMORBIDITY | ||||
| Cardiovascular disease, n (%) | 791 (48) | 346 (44) | 445 (52) | <0.001 |
| Pulmonary disease, n (%) | 185 (11) | 79 (10) | 106 (13) | NS |
| Non-AIDS malignancies, n (%) | 110 (7) | 64 (8) | 46 (5) | 0.03 |
| HIV SEVERITY | ||||
| HIV-infected, n (%) | 814 (47) | |||
| On antiretroviral therapy, n (%) | 762 (94) | |||
| HIV severity | ||||
| HIV RNA Viral load, median (IQR) | 40 (20–91) | |||
| CD4+ count, median (IQR) | 483 (308–691) | |||
| VACS Index 2.0, median (IQR) | 42 (33–55) | 51 (41.5–63) | 35 (29–44) | <0.001 |
| HEALTH CARE UTILIZATION | ||||
| Outpatient visits in last year, median (IQR) | 4 (2–6) | 4 (3–7) | 3 (2–5) | <0.001 |
| Inpatient visits in last year, median (IQR) | 0 (0–1) | 0 (0–0) | 0 (0–1) | <0.001 |
| SMOKING-RELATED ATTITUDES / BEHAVIORS | ||||
| Contemplation to quit, n (%) | 1175 (71) | 554 (72) | 621 (71) | NS |
| Past quit attempts, median (IQR) | 3 (2–5) | 3 (2–5) | 3 (2–5) | NS |
| Cigarettes smoked daily, median (IQR) | 9(5–12) | 8(5–12) | 10 (5–12) | NS |
| Smoke >1 pack daily, n (%) | 63 (4) | 27 (4) | 36 (4) | NS |
| Time to first cigarette, n (%) | NS | |||
| After 60 minutes | 493 (29) | 242 (30) | 251 (28) | |
| 31–60 minutes | 290 (17) | 134 (17) | 156 (18) | |
| 6–30 minutes | 486 (29) | 225 (28) | 261 (29) | |
| Within 5 minutes | 425 (25) | 206 (26) | 219 (25) | |
There were differences between PLWH and HIV-uninfected participants. Compared with uninfected participants, PLWH had a greater proportion of people reporting black race and earning $12,000-$24,999 annually, a lower prevalence of recent non-AIDS malignancy diagnosis, a higher median VACS Index 2.0 score, and a greater number of outpatient visits in the last year. PLWH had a lower prevalence of unhealthy alcohol use, moderate-severe depression, recent psychiatric and CVD diagnoses, and fewer inpatient visits in the last year relative to uninfected participants (Table 1). However, no differences by HIV status were observed in the proportion of participants who were contemplating smoking cessation, median number of previous quit attempts, median number of cigarettes smoked daily, or time to first cigarette indicating degree of nicotine dependence (Table 1). PLWH were more likely to use marijuana, but there were no other differences in substance use between groups.
Receipt of Smoking Cessation Pharmacotherapy by HIV Status
Overall, the prevalence of receiving any pharmacotherapy was 33% (n=1722); and the prevalence of receiving NRT, buproprion, and varenicline was 22%, 9%, and 1%, respectively. The prevalence of receiving any smoking cessation pharmacotherapy was lower among PLWH than among uninfected patients (29% vs. 36%, respectively; p=0.004) (Table 2). With regard to specific medications, the percent receiving NRT was lower among PLWH than HIV-uninfected participants (19% vs. 25%, respectively; p=0.002), no differences were observed in receipt of buproprion (8% vs. 9%; p=NS), and PLWH had a slightly higher percent of receipt of varenicline than uninfected participants (1% vs. 0.4%; p=0.03) (Table 2).
Table 2.
Associations between HIV status and Receipt of Smoking Cessation Medications among Patients Recruited to the Veterans Aging Cohort Study (N=1722): Results of Bivariate Comparisons and Adjusted Log-Linear Regression Models
| HIV+ (n=814) |
HIV− (n=908) |
Unadjusted relative risk | Adjusted relative risk | ||||||
|---|---|---|---|---|---|---|---|---|---|
| N | (%) | N | (%) | p-value | RR | (95% CI) | ARR* | (95% CI) | |
| Receipt of Smoking Cessation Medications | |||||||||
| Any medication | 237 | (29) | 323 | (36) | 0.004 | 0.82 | 0.71, 0.94 | 0.90 | 0.76, 1.07 |
| Nicotine | 154 | (19) | 229 | (25) | 0.002 | 0.75 | 0.58, 0.97 | 0.77 | 0.67, 0.89 |
| replacement therapy | |||||||||
| Bupropion | 69 | (8) | 84 | (9) | NS | 0.97 | 0.75, 1.26 | 1.25 | 0.87, 1.80 |
| Varenicline | 12 | (1) | 4 | (0.4) | 0.03 | 3.02 | 0.43, 21.00 | -- | -- |
Relative risk of receiving medications for HIV+ relative to HIV- adjusted for age, sex, race/ethnicity, education, income, unhealthy alcohol use, substance use in last year, moderate-severe depression, recent diagnoses for psychiatric disorders, CVD, chronic pulmonary disease and non-AIDS malignancies, VACS Index 2.0, number of inpatient and outpatient visits, contemplation to quit, past quit attempt, and number of cigarettes smoked daily.
In unadjusted models, the relative risk of receiving any smoking cessation pharmacotherapy, and NRT specifically, was lower among PLWH relative to uninfected patients (RR 0.81, 95% CI 0.71, 0.94 and RR 0.75, 95% CI 0.58, 0.97, respectively). After adjustment for all covariates, no statistically significant differences in HIV status were observed in the primary outcome—receipt of any smoking cessation pharmacotherapy (Table 2). However, PLWH were less likely than uninfected persons to receive NRT after full adjustment (ARR 0.77, 95% CI 0.67, 0.89). No statistically significant difference was observed in receipt of bupropion and, due to the small number of observations, the model assessing varenicline receipt did not converge (Table 2).
Patient-Level Factors Associated with Receipt of Pharmacotherapy among PLWH
Factors associated with increased likelihood of receiving smoking cessation pharmacotherapy among PLWH included: recently diagnosed psychiatric disorder (ARR 1.34, 95% CI 1.03, 1.73), recently diagnosed pulmonary disease (ARR 1.40, 95% CI 1.03, 1.90), and contemplation to quit (ARR 1.85, 95% CI 1.10, 3.10) (Table 3). There were no factors associated with decreased likelihood of receiving pharmacotherapy among PLWH.
Table 3:
Relative Risk for Receipt of Any Smoking Cessation Medication Associated with Patient Characteristics Stratified by HIV Status among Patients Recruited to the Veterans Aging Cohort Study (N=1722)
| HIV+ ARR* (95% CI) |
HIV− ARR* (95% CI) |
|
|---|---|---|
| SOCIODEMOGRAPHIC FACTORS | ||
| Age | 1.00 (0.98, 1.01) | 0.99 (0.98, 1.00) |
| Male | 1.46 (0.90, 2.37) | 0.86 (0.53, 1.37) |
| Race/ethnicity | ||
| White | Reference | Reference |
| Black | 0.73 (0.48, 1.10) | 1.25 (1.00, 1.57) |
| Hispanic | 0.64 (0.36, 1.13) | 1.10 (0.72, 1.68) |
| Other | 0.97 (0.53, 1.75) | 1.75 (1.24, 2.47) |
| Education | ||
| < High school | Reference | Reference |
| High school grad, GED | 0.68 (0.32, 1.43) | 0.62 (0.35, 1.09) |
| Some college or technical school | 0.69 (0.36, 1.32) | 0.72 (0.48, 1.09) |
| College grad, higher education | 0.96 (0.40, 2.28) | 0.60 (0.23, 1.54) |
| Annual income | ||
| <$1200 | Reference | Reference |
| $1200–24999 | 0.92 (0.68, 1.23) | 0.84 (0.71, 0.98) |
| ≥ $25000 | 0.94 (0.68, 1.29) | 0.92 (0.77, 1.09) |
| MENTAL HEALTH / SUBSTANCE USE | ||
| Unhealthy alcohol use | 0.99 (0.82, 1.20) | 1.05 (0.85, 1.29) |
| Substance use in the last year | 0.97 (0.78, 1.19) | 0.86 (0.69, 1.07) |
| Moderate-severe depression | 0.90 (0.76, 1.06) | 1.33 (1.12, 1.59) |
| Psychiatric disorders | 1.34 (1.03, 1.73) | 1.83 (1.53, 2.20) |
| SMOKING-RELATED COMORBIDITY | ||
| Cardiovascular disease | 1.04 (0.72, 1.50) | 0.98 (0.79, 1.21) |
| Pulmonary disease | 1.40 (1.03, 1.90) | 1.17 (0.96, 1.43) |
| Non-AIDS malignancies | 1.26 (0.92, 1.72) | 1.32 (0.88, 1.99) |
| HIV SEVERITY | ||
| VACS Index 2.0 | 0.99 (0.98, 1.01) | 1.00 (0.99, 1.00) |
| HEALTH CARE UTILIZATION | ||
| Outpatient visits in last year | ||
| 1 to 2 | Reference | Reference |
| 3 to 5 | 1.28 (0.75, 2.20) | 0.92 (0.77, 1.09) |
| ≥6 | 1.63 (0.95, 2.79) | 1.01 (0.87, 1.15) |
| Inpatient visits in last year | ||
| 0 | Reference | Reference |
| 1 | 1.02 (0.71, 1.45) | 1.19 (0.87, 1.63) |
| ≥2 | 1.12 (0.77, 1.64) | 0.91 (0.70, 1.18) |
| SMOKING-RELATED ATTITUDES/BEHAVIOR | ||
| Contemplation to quit | 1.85 (1.10, 3.10) | 1.31 (1.14, 1.50) |
| Past quit attempt | 1.31 (0.59, 2.88) | 2.27 (1.57, 3.28) |
| Cigarettes smoked daily | ||
| <10 | Reference | Reference |
| 10–20 | 1.26 (0.81, 1.96) | 1.19 (1.06, 1.33) |
| >20 | 1.51 (0.73, 3.14) | 0.96 (0.76, 1.22) |
Relative risk of receiving medications adjusted for age, sex, race/ethnicity, education, income, unhealthy alcohol use, substance use in last year, moderate-severe depression, recent diagnoses for psychiatric disorders CVD, chronic pulmonary disease, and non-AIDS malignancies, VACS Index 2.0, number of outpatient and inpatient visits, contemplation to quit, past quit attempt, and number of cigarettes smoked daily.
Patient-Level Factors Associated with Receipt of Pharmacotherapy among HIV-uninfected
Factors associated with increased likelihood of receiving smoking cessation pharmacotherapy among uninfected participants included: black race (ARR 1.25, 95% CI 1.00, 1.57) and “other” race (ARR 1.75, 95% CI 1.24, 2.47) relative to white race, moderate to severe depression (ARR 1.33, 95% CI 1.12, 1.59), recently diagnosed psychiatric disorder (ARR 1.83, 95% CI 1.53, 2.20), contemplation to quit (ARR 1.31, 95% CI 1.14, 1.50), past quit attempt (ARR 2.27, 95% CI 1.57, 3.28), and report of smoking 10–20 cigarettes daily (ARR 1.19, 95% CI 1.06, 1.33) compared to report of smoking <10 cigarettes daily (Table 3). Earning $12,000–24,999 annually was associated with decreased likelihood of receiving smoking cessation pharmacotherapy relative to earning less than $12,000 annually (ARR 0.84, 95% CI 0.71, 0.98) (Table 3).
Discussion
To our knowledge, this is the first study to investigate receipt of effective pharmacologic treatment for tobacco use among PLWH who currently smoke, both overall and relative to a matched cohort of uninfected controls. After adjustment for covariates, PLWH were less likely to receive NRT and had comparable rates of receiving other smoking cessation pharmacotherapy with HIV-uninfected participants. Our findings suggest underutilization of pharmacotherapies generally, and disproportionate underutilization of NRT among PLWH for whom the health risks of smoking are increased.
Of note, over 70% of participants reported contemplating cessation and the median number of previous quit attempts was three in our sample. No significant differences in these characteristics were observed between those with and without HIV, suggesting high levels of readiness to stop smoking across groups. Nonetheless, a small proportion of all participants filled prescriptions for effective pharmacotherapies in the year following survey. Further work is needed to increase access to these effective pharmacotherapies, especially among highly motivated patients. Promotion of specific medications may also be necessary. Though varenicline has been demonstrated to have the greatest efficacy to promote cessation in populations with and without HIV (8,40–43), it was the least prescribed pharmacotherapy. This may relate to the fact that varenicline is still considered a second-line treatment and is strictly controlled within the VHA due to several cases specifically involving Veterans in which it potentially exacerbated underlying mental illness (44). Despite robust evidence demonstrating safety (45), clinicians should closely monitor patients who are taking varenicline, similarly for any smoking cessation medication, as nicotine withdrawal may result in neuropsychiatric symptoms (46).
Although NRT was the most frequently received pharmacotherapy, the vast majority of participants did not receive it, and rates were lower among PLWH than uninfected persons. Prior studies have demonstrated that PLWH who report smoking have substantial interest in NRT—one conducted in a sample of 60 patients receiving care at an ID clinic in Bronx, New York found that 64% of participants were interested in NRT (47), and another conducted among 123 patients from an ID clinic in San Francisco, California found that 82% of PLWH who were currently thinking about quitting were interested in NRT (48). Whether there is high interest in NRT among PLWH receiving VHA care who report smoking is unknown, but low rates of NRT across groups and lower rates among PLWH than uninfected persons in the present study suggest room for improvement with regard to provision of smoking cessation pharmacotherapy.
Patient-level factors associated with the receipt of pharmacotherapy were both similar and different for PLWH and uninfected groups. Recently documented psychiatric disorder and contemplation to quit were associated with increased likelihood of pharmacotherapy receipt for both participants with and without HIV. However, sociodemographic characteristics (e.g., race and income) appeared to be important predictors of receiving pharmacotherapy in the uninfected population whereas they were not associated with pharmacotherapy receipt among PLWH. These findings suggest the possibility of inequitable receipt of pharmacotherapy among uninfected persons. However, given that this sample of HIV-uninfected adults was matched to PLWH based on age, sex, race, and site of care, it is unclear whether these findings are generalizable to a broader population of uninfected persons receiving VHA care. Thus, further work is needed to assess patient factors that predict receipt of smoking cessation pharmacotherapy among uninfected Veterans with current smoking in broader samples.
Overall, reasons for differences in smoking cessation pharmacotherapy receipt across HIV status and patient-level predictors identified in the present study are unclear. Factors both inside and outside of the medical encounter may have contributed to these disparities, including patient-level factors (e.g., preferences), provider-level factors (e.g., knowledge and training), and clinical setting (e.g., resources available), many of which were not available measures in our study. For instance, PLWH may be receiving care from providers with different levels of knowledge and training than uninfected patients, or a greater proportion of PLWH may prefer non-pharmacologic options. Unfortunately, we were not able to differentiate such patient and provider-level factors. Similarly in the present study, a recent diagnosis for pulmonary disease among PLWH was associated with increased likelihood of receiving pharmacotherapy. A previous study in VACS found that PLWH with a recent diagnosis for pulmonary disease had nearly five-times the odds of having made a quit attempt in the last year relative to those without such a diagnosis (28). Thus, it is possible that receiving a diagnosis for a pulmonary disease increases patient readiness to quit smoking. However, it is also possible that identification of a pulmonary condition by a provider may also increase likelihood that the provider gains knowledge about and/or becomes more willing to prescribe pharmacotherapy. Future research is needed to explore patient treatment preferences, as well as provider- and clinic-level factors that are likely influential in patients’ receipt of pharmacotherapies.
Interestingly, substance use and depression were not associated with receipt of smoking cessation pharmacotherapy in either PLWH or HIV-uninfected participants despite previous studies citing these as putative barriers to smoking cessation (49–54). Specifically among PLWH, substance use and depression have been found to be associated with current smoking status, greater nicotine dependence, and lack of motivation to quit (14,47,52–54). Moderate to severe depression was significantly associated with receipt of pharmacotherapy among the uninfected group though not among PLWH. Unhealthy alcohol use and substance use in the last year were not associated with pharmacotherapy receipt in either group. Yet, these factors associated with both smoking and HIV infection may not be translatable to provider-level practices and preferences in offering their patients smoking cessation pharmacotherapies.
Of note, recent diagnosis for CVD was not associated with receipt of smoking cessation pharmacotherapy for either group. Among the many comorbid health conditions afflicting PLWH who smoke, CVD has become of particular concern due to ART-induced metabolic changes, the high prevalence of cardiovascular risk factors in PLWH, and accelerated inflammatory processes that are believed to promote atherothrombosis (55). Smoking is a well-known and modifiable risk factor for CVD with marked reductions in CVD and coronary heart disease in PLWH who have successfully quit (5). Therefore, these diagnoses of CVD may represent missed opportunities to promote smoking cessation in this population. Promoting smoking cessation, as well as greater provision and access to pharmacotherapy is crucial among PLWH who face severe health consequences due to smoking.
In addition to limitations regarding measurement of patient-, provider-, and clinic-level factors that may influence receipt of pharmacotherapy, this study is limited in several ways. Specifically, this cohort was comprised of Veterans receiving care in the VHA and results may not be generalizable to non-Veterans, Veterans receiving VHA care who do not match the epidemiologic profile of PLWH receiving VHA care, persons not receiving health care, or patients with and without HIV receiving care in other health care systems. Additionally, NRT is available over the counter and it is possible that participants have procured smoking cessation medications in pharmacies outside the VHA. However, due to low copayments within the VHA, Veterans are not likely to obtain their medications elsewhere (56–58).
Despite these limitations, this study is among the first to assess receipt of smoking cessation pharmacotherapies among PLWH and well-matched HIV-uninfected Veterans from nine major U.S. cities. No differences in any pharmacotherapy was observed by HIV status, though PLWH were less likely than HIV-uninfected participants to receive NRT. Further work is needed to ensure access to effective pharmacologic treatments for patients who report smoking, particularly those with HIV for whom the dangers of smoking are great.
Acknowledgments
This research was supported by the COMpAAAS/Veterans Aging Cohort Study, which is a CHAART Cooperative Agreement funded by the National Institutes of Health: National Institute on Alcohol Abuse and Alcoholism (U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799; U10 AA013566); and the National Institutes of Health: National Heart, Lung, and Blood Institute (U01HL142103, R01CA243907)
Alphabetical list of abbreviations:
- AIDS
Acquired immunodeficiency syndrome
- ART
Antiretroviral therapy
- ARR
Adjusted relative risk
- AUDIT-C
Alcohol Use Disorders Identification Test Consumption
- CDW
Corporate Data Warehouse
- CI
Confidence interval
- COPD
Chronic obstructive pulmonary disease
- CVD
Cardiovascular disease
- EHR
Electronic health record
- FDA
Food and Drug Administration
- GED
General education development
- HIV
Human immunodeficiency virus
- ICD-9-CM
International Classification of Disease, Ninth Revision, Clinical Modification
- ID
Infectious disease
- NRT
Nicotine replacement therapy
- PHQ-9
Patient Health Questionnaire
- PLWH
People living with HIV
- RR
Relative risk
- VACS
Veterans Aging Cohort Study
- VHA
Veterans Health Administration
Footnotes
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Disclosures: None.
Conflict of interest: None.
References
- 1.Niaura R, Shadel WG, Morrow K, Tashima K, Flanigan T, Abrams DB. Human immunodeficiency virus infection, AIDS, and smoking cessation: the time is now. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2000;31:808–12. [DOI] [PubMed] [Google Scholar]
- 2.Mocroft A, Ledergerber B, Katlama C et al. Decline in the AIDS and death rates in the EuroSIDA study: an observational study. Lancet (London, England) 2003;362:22–9. [DOI] [PubMed] [Google Scholar]
- 3.Sudano I, Spieker LE, Noll G, Corti R, Weber R, Luscher TF. Cardiovascular disease in HIV infection. American heart journal 2006;151:1147–55. [DOI] [PubMed] [Google Scholar]
- 4.Lifson AR, Neuhaus J, Arribas JR, van den Berg-Wolf M, Labriola AM, Read TR. Smoking-related health risks among persons with HIV in the Strategies for Management of Antiretroviral Therapy clinical trial. American journal of public health 2010;100:1896–903. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Petoumenos K, Worm S, Reiss P et al. Rates of cardiovascular disease following smoking cessation in patients with HIV infection: results from the D:A:D study(*). HIV medicine 2011;12:412–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Freiberg MS, Chang CC, Kuller LH et al. HIV infection and the risk of acute myocardial infarction. JAMA Intern Med 2013;173:614–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ingersoll KS, Cropsey KL, Heckman CJ. A test of motivational plus nicotine replacement interventions for HIV positive smokers. AIDS and behavior 2009;13:545–554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ferketich AK, Diaz P, Browning KK et al. Safety of varenicline among smokers enrolled in the lung HIV study. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 2013;15:247–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Mercie P, Arsandaux J, Katlama C et al. Efficacy and safety of varenicline for smoking cessation in people living with HIV in France (ANRS 144 Inter-ACTIV): a randomised controlled phase 3 clinical trial. The lancet HIV 2018. [DOI] [PubMed] [Google Scholar]
- 10.Pedrol-Clotet E, Deig-Comerma E, Ribell-Bachs M, Vidal-Castell I, Garcia-Rodriguez P, Soler A. [Bupropion use for smoking cessation in HIV-infected patients receiving antiretroviral therapy]. Enfermedades infecciosas y microbiologia clinica 2006;24:509–11. [DOI] [PubMed] [Google Scholar]
- 11.Humfleet GL, Hall SM, Delucchi KL, Dilley JW. A randomized clinical trial of smoking cessation treatments provided in HIV clinical care settings. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 2013;15:1436–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cui Q, Robinson L, Elston D et al. Safety and tolerability of varenicline tartrate (Champix((R))/Chantix((R))) for smoking cessation in HIV-infected subjects: a pilot open-label study. AIDS patient care and STDs 2012;26:12–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Kalkhoran S, Benowitz NL, Rigotti NA. Prevention and Treatment of Tobacco Use: JACC Health Promotion Series. Journal of the American College of Cardiology 2018;72:1030–1045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Burkhalter JE, Springer CM, Chhabra R, Ostroff JS, Rapkin BD. Tobacco use and readiness to quit smoking in low-income HIV-infected persons. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 2005;7:511–22. [DOI] [PubMed] [Google Scholar]
- 15.Duval X, Baron G, Garelik D et al. Living with HIV, antiretroviral treatment experience and tobacco smoking: results from a multisite cross-sectional study. Antiviral therapy 2008;13:389–397. [PMC free article] [PubMed] [Google Scholar]
- 16.Humfleet GL, Delucchi K, Kelley K, Hall SM, Dilley J, Harrison G. Characteristics of HIV-positive cigarette smokers: a sample of smokers facing multiple challenges. AIDS education and prevention : official publication of the International Society for AIDS Education 2009;21:54–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Crothers K, Goulet JL, Rodriguez-Barradas MC et al. Decreased awareness of current smoking among health care providers of HIV-positive compared to HIV-negative veterans. Journal of General Internal Medicine 2007;22:749–54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Tesoriero JM, Gieryic SM, Carrascal A, Lavigne HE. Smoking among HIV positive New Yorkers: prevalence, frequency, and opportunities for cessation. AIDS and behavior 2010;14:824–35. [DOI] [PubMed] [Google Scholar]
- 19.Shuter J, Salmo LN, Shuter AD, Nivasch EC, Fazzari M, Moadel AB. Provider beliefs and practices relating to tobacco use in patients living with HIV/AIDS: a national survey. AIDS and behavior 2012;16:288–294. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.U.S. Department of Health and Human Services HRaSA. Guide for HIV/AIDS Clinical Care – 2014. Edition. Rockville, MD: US Department of Health and Human Services. [Google Scholar]
- 21.Justice AC, Dombrowski E, Conigliaro J et al. Veterans Aging Cohort Study (VACS): Overview and description. Medical care 2006;44:S13–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Price LE, Shea K, Gephart S. The Veterans Affairs’s Corporate Data Warehouse: Uses and Implications for Nursing Research and Practice. Nursing administration quarterly 2015;39:311–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Fultz SL, Skanderson M, Mole LA et al. Development and verification of a “virtual” cohort using the National VA Health Information System. Medical care 2006;44:S25–30. [DOI] [PubMed] [Google Scholar]
- 24.Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcoholism, clinical and experimental research 2007;31:1208–17. [DOI] [PubMed] [Google Scholar]
- 25.Saitz R Clinical practice. Unhealthy alcohol use. The New England journal of medicine 2005;352:596–607. [DOI] [PubMed] [Google Scholar]
- 26.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 2001;16:606–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Braun R, Rybarz A, Schulz M, Thierbach R. [Introduction of the 9th Revision of the International Statistical Classification of Diseases, Injuries and Causes of Death]. Zeitschrift fur die gesamte Hygiene und ihre Grenzgebiete 1978;24:793–800. [PubMed] [Google Scholar]
- 28.Shahrir S, Tindle HA, McGinnis KA et al. Contemplation of smoking cessation and quit attempts in human immunodeficiency virus-infected and uninfected veterans. Substance abuse 2016;37:315–22. [DOI] [PubMed] [Google Scholar]
- 29.Tate JP, Sterne JAC, Justice AC. Albumin, white blood cell count, and body mass index improve discrimination of mortality in HIV-positive individuals. AIDS (London, England) 2019;33:903–912. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Akgün KM, Tate JP, Crothers K et al. An adapted frailty-related phenotype and the VACS index as predictors of hospitalization and mortality in HIV-infected and uninfected individuals. Journal of acquired immune deficiency syndromes (1999) 2014;67:397–404. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Akgun KM, Tate JP, Pisani M et al. Medical ICU admission diagnoses and outcomes in human immunodeficiency virus-infected and virus-uninfected veterans in the combination antiretroviral era. Critical care medicine 2013;41:1458–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Bebu I, Tate J, Rimland D et al. The VACS index predicts mortality in a young, healthy HIV population starting highly active antiretroviral therapy. Journal of acquired immune deficiency syndromes (1999) 2014;65:226–230. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Brown ST, Tate JP, Kyriakides TC et al. The VACS index accurately predicts mortality and treatment response among multi-drug resistant HIV infected patients participating in the options in management with antiretrovirals (OPTIMA) study. PLoS One 2014;9:e92606–e92606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Justice AC, Modur SP, Tate JP et al. Predictive accuracy of the Veterans Aging Cohort Study index for mortality with HIV infection: a North American cross cohort analysis. Journal of acquired immune deficiency syndromes (1999) 2013;62:149–163. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Williams EC, Lapham GT, Shortreed SM et al. Among patients with unhealthy alcohol use, those with HIV are less likely than those without to receive evidence-based alcohol-related care: A national VA study. Drug and alcohol dependence 2017;174:113–120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Greenland S Model-based estimation of relative risks and other epidemiologic measures in studies of common outcomes and in case-control studies. American journal of epidemiology 2004;160:301–5. [DOI] [PubMed] [Google Scholar]
- 37.Zou G A modified poisson regression approach to prospective studies with binary data. American journal of epidemiology 2004;159:702–6. [DOI] [PubMed] [Google Scholar]
- 38.Liang K-Y, Zeger SL. Longitudinal Data Analysis Using Generalized Linear Models. Biometrika 1986;73:13–22. [Google Scholar]
- 39.Lapham GT, Achtmeyer CE, Williams EC, Hawkins EJ, Kivlahan DR, Bradley KA. Increased Documented Brief Alcohol Interventions With a Performance Measure and Electronic Decision Support. Medical care 2012;50:179–187. [DOI] [PubMed] [Google Scholar]
- 40.Brose LS, West R, Stapleton JA. Comparison of the effectiveness of varenicline and combination nicotine replacement therapy for smoking cessation in clinical practice. Mayo Clinic proceedings 2013;88:226–33. [DOI] [PubMed] [Google Scholar]
- 41.Cahill K, Stevens S, Perera R, Lancaster T. Pharmacological interventions for smoking cessation: an overview and network meta-analysis. The Cochrane database of systematic reviews 2013:Cd009329. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Mills EJ, Wu P, Lockhart I, Thorlund K, Puhan M, Ebbert JO. Comparisons of high-dose and combination nicotine replacement therapy, varenicline, and bupropion for smoking cessation: a systematic review and multiple treatment meta-analysis. Annals of medicine 2012;44:588–97. [DOI] [PubMed] [Google Scholar]
- 43.Kralikova E, Kmetova A, Stepankova L, Zvolska K, Davis R, West R. Fifty-two-week continuous abstinence rates of smokers being treated with varenicline versus nicotine replacement therapy. Addiction (Abingdon, England) 2013;108:1497–502. [DOI] [PubMed] [Google Scholar]
- 44.Cantrell M, Argo T, Haak L, Janney L. Adverse neuropsychiatric events associated with varenicline use in veterans: a case series. Issues in mental health nursing 2012;33:665–9. [DOI] [PubMed] [Google Scholar]
- 45.Anthenelli RM, Benowitz NL, West R et al. Neuropsychiatric safety and efficacy of varenicline, bupropion, and nicotine patch in smokers with and without psychiatric disorders (EAGLES): a double-blind, randomised, placebo-controlled clinical trial. The Lancet 2016;387:2507–2520. [DOI] [PubMed] [Google Scholar]
- 46.Ebbert JO, Wyatt KD, Hays JT, Klee EW, Hurt RD. Varenicline for smoking cessation: efficacy, safety, and treatment recommendations. Patient Prefer Adherence 2010;4:355–362. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Shuter J, Bernstein SL, Moadel AB. Cigarette smoking behaviors and beliefs in persons living with HIV/AIDS. Am J Health Behav 2012;36:75–85. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Mamary EM, Bahrs D, Martinez S. Cigarette smoking and the desire to quit among individuals living with HIV. AIDS patient care and STDs 2002;16:39–42. [DOI] [PubMed] [Google Scholar]
- 49.Williams JM, Ziedonis D. Addressing tobacco among individuals with a mental illness or an addiction. Addictive behaviors 2004;29:1067–83. [DOI] [PubMed] [Google Scholar]
- 50.Ziedonis D, Hitsman B, Beckham JC et al. Tobacco use and cessation in psychiatric disorders: National Institute of Mental Health report. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 2008;10:1691–715. [DOI] [PubMed] [Google Scholar]
- 51.Jackson KM, Sher KJ, Cooper ML, Wood PK. Adolescent alcohol and tobacco use: onset, persistence and trajectories of use across two samples. Addiction (Abingdon, England) 2002;97:517–531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Gritz ER, Vidrine DJ, Lazev AB, Amick BC 3rd, Arduino RC. Smoking behavior in a low-income multiethnic HIV/AIDS population. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 2004;6:71–7. [DOI] [PubMed] [Google Scholar]
- 53.Marshall MM, Kirk GD, Caporaso NE et al. Tobacco use and nicotine dependence among HIV-infected and uninfected injection drug users. Addictive behaviors 2011;36:61–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Stewart DW, Jones GN, Minor KS. Smoking, depression, and gender in low-income African Americans with HIV/AIDS. Behavioral medicine (Washington, DC) 2011;37:77–80. [DOI] [PubMed] [Google Scholar]
- 55.Triant VA. Cardiovascular disease and HIV infection. Current HIV/AIDS reports 2013;10:199–206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Borowsky SJ, Cowper DC. Dual use of VA and non-VA primary care. Journal of General Internal Medicine 1999;14:274–280. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Kashner TM, Muller A, Richter E, Hendricks A, Lukas CV, Stubblefield DR. Private health insurance and veterans use of Veterans Affairs care. RATE Project Committee. Rate Alternative Technical Evaluation. Medical care 1998;36:1085–97. [DOI] [PubMed] [Google Scholar]
- 58.Wolinsky FD, Coe RM, Mosely RR 2nd, Homan SM. Veterans’ and nonveterans’ use of health services. A comparative analysis. Medical care 1985;23:1358–71. [DOI] [PubMed] [Google Scholar]