Abstract
Background
Neurocognitive impairment is a frequent and often disabling comorbidity of HIV infection. In addition to antiretroviral (ARV) therapies, individuals with HIV infection may commonly use non-ARV medications that are known to cause neurocognitive adverse effects (NC-AE). The contribution of NC-AE to neurocognitive impairment is rarely considered in the context of HIV and could explain part of the variability in neurocognitive performance among individuals with HIV.
Setting
Women’s Interagency HIV Study (WIHS); a prospective, multisite, observational study of U.S. women with and without HIV.
Methods
After a literature review, 79 medications (excluding statins) with NC-AE were identified and reported by WIHS participants. We examined factors associated with self-reported use of these medications over a 10-year period. Generalized estimating equations for binary outcomes were used to assess socio-demographic, behavioral, and clinical characteristics associated with NC-AE medication use.
Results
3,300 women (71% with HIV) and data from ~42,000 visits were studied. HIV infection was associated with NC-AE medication use (odds ratio =1.52 (95% confidence interval:1.35–1.71)). After adjustment for HIV infection status, other predictors of NC-AE medication use included having health insurance, elevated depressive symptoms, prior clinical AIDS, non-injection recreational drug use, and an annual household income <$12,000 (p’s<0.004). NC-AE medication use was less likely among women who drank 1–7 or 8–12 alcoholic drinks/week (vs. abstaining) (p’s<0.04).
Conclusions
HIV infection was associated with NC-AE medication use which may influence determinations of HIV-associated neurocognitive impairment. Providers should consider the impact of NC-AE medications when evaluating patients with HIV and concurrent neurocognitive symptoms.
Keywords: neurocognition, medication, adverse effects, dementia, HIV
INTRODUCTION
Despite effective antiretroviral (ARV) therapy, people living with HIV continue to report memory and mental acuity problems and demonstrate impairment on standard measures of neuropsychological functioning.1,2 For example, in the Women’s Interagency HIV Study (WIHS), HIV-infected (HIV+) women on effective ARVs show persistent vulnerabilities in global neuropsychological functioning as well as in verbal learning, memory, attention/working memory, and verbal fluency compared to HIV-uninfected (HIV−) women.3 Moreover, issues in motor function become apparent over time among HIV+ women on ARVs versus HIV− women. Given the persistence of neurocognitive vulnerabilities despite combined antiretroviral therapy (cART) and their relationship to function4,5, identifying potential contributors to neurocognitive performance is an important clinical priority.
One factor that may influence some of the variability in neuropsychological test performance among HIV+ individuals is the effects of non-ARV medications with known neurocognitive adverse effects (NC-AE) including agents with anticholinergic properties, anxiolytics, antipsychotics, antiepileptics, and opiates.2,6–11 The possible contribution of non-ARV medications to neurocognitive performance in HIV+ individuals is particularly important to consider since cART recipients are living longer and using multiple non-ARV medications with age.12,13 On average, HIV+ individuals report using 7–14 non-ARV medications many of which are NC-AE medications.14–16 Importantly, concomitant medication use or “polypharmacy” is associated with lower performance on rapid screening tests for cognitive impairment in both HIV+17 and HIV− individuals.18
An important first step before investigating NC-AE medication associations with neuropsychological test performance is to: 1) characterize the patterns and prevalence of NC-AE medication use ; 2) determine socio-demographic, behavioral, and clinical predictors of NC-AE medication use; and 3) to determine whether NC-AE medication use predicts HIV-related treatment outcomes. We addressed these aims within the WIHS and hypothesized that HIV-serostatus would predict NC-AE medication use and that NC-AE medication use would predict lower cART adherence and virologic suppression.
METHODS
Participants
All data were prospectively collected at semi-annual WIHS visits; methods were previously published.19–21 HIV+ and HIV− women were enrolled in the WIHS at any of 11 sites across the United States between 1994 and 2014 (enrollment dates vary by site and study wave). All participants provided written informed consent via human subject’s protection protocols approved by each of the collaborating institutions. Analyzable participant data was limited to WIHS visits with non-ARV medication data available occurring in the era of optimized cART regimens. Specifically, we included 21 visits beginning April 2004 through September 2014 (data available at the time of analysis). Women with incident HIV infection after WIHS enrollment were excluded (n=25).
Data collection
Self-reported socio-demographic and medication use data were obtained via interviewer-administered survey instruments, and HIV-relevant laboratory measurements were recorded from specimen analysis at study visits.20 For medication use, participants were asked to recall ARV medications taking currently and since last study visit (typically six months), medications for specific conditions of interest, and any other medications used since last visit.
Defining NC-AE medication use
A literature search was conducted using UpToDate® and PubMed® to identify non-ARV NC-AE medications. The search included a combination of terms related to central nervous system (CNS) impairment (e.g., “cognit* AND impair*”), medication use (e.g., “med*”, “drug”), and adverse effects (e.g., “adverse”). Medication classes (e.g., antidepressants) identified through this search were further explored through additional, more specific search terms. In order for a medication to make NC-AE classification, reports must have described the specific adverse effect (e.g., memory loss) associated with a specific medication. Medications with CNS adverse effects but not those impairing cognition (e.g., headache) were not accepted as NC-AE classification. Both primary and review articles were accepted as sources. The NC-AE for each identified medication was verified using a second resource, Lexicomp Online.
Using these methods, 102 non-ARV medications were identified as having NC-AE properties and 83/102 were reported to be used by WIHS participants. Each NC-AE medication was assigned an individual code as well as one group code determined by the medication’s drug class. Earlier studies report that hydroxymethylglutaryl coenzyme A (HMG-CoA) reductase inhibitors (statins) were associated with neurocognitive impairment; however, recent systematic assessments indicate that statins are not likely to cause neurocognitive impairment and could possibly prevent it.22–24 The association between statin use and neurocognitive impairment is most likely due to their major indication of the treatment of hyperlipidemias, a condition that increases the risk of vascular-based neurological injury25 We suspect that statins likely differ from other NC-AE medications that have more direct effects on cognition. Thus, we excluded statins from the list of NC-AE medications, leaving 79 NC-AE medications in 11 classification groups (see Table, Supplemental Digital Content 1, for list of NC-AE medications). Each potential NC-AE medication identified in the WIHS dataset was further ascertained by a Doctor of Pharmacy candidate (K.R.) to ensure accurate NC-AE classifications. The WIHS database was searched by brand name, generic name, and indication as well as potential variations in spellings of NC-AE medication names. Individual participants were classified as an ever (versus never) NC-AE medication user based on whether they reported using at least one NC-AE medication at any time. Visits were considered an NC-AE medication use visit if at least one NC-AE medication was reported.
Predictors of NC-AE medication use
Fixed-factors were summarized by unique WIHS participant (N=3,300) and included HIV infection status, race/ethnicity, enrollment site, and educational attainment (high school/GED/diploma). Time-varying factors were summarized by unique WIHS visit and included: age, annual household income (>$12,000), injection and non-injection recreational drug use (RDU), alcohol use (abstain, 1–7 drinks/week, 8–12 drinks/week, or >12 drinks/week), elevated depressive symptoms (Center for Epidemiologic Studies Depression Scale ≥ 16), homelessness (residence of street, shelter/welfare hotel, or rooming/boarding/halfway house), third-party health payer (any private/public health or dental insurance or medication cost assistance payer), clinical AIDS (any criterion excluding CD4 cell count), and undetectable plasma HIV RNA (values below assay threshold ranged from 50–80cp/ml during the study period).
Statistical Analyses
Basic summary statistics (e.g., mean, standard deviation) were used to summarize participant characteristics. Generalized estimating equations for discrete outcomes were conducted to assess factors associated with any NC-AE medication use and to assess any NC-AE medication use as a predictor of HIV-related clinical outcomes including cART use, ≥ 95% cART adherence, undetectable plasma HIV RNA (viral load). All models were fit using PROC GENMOD (exchangeable correlation structure) in SAS version 9.4 (SAS Institute, Cary, NC). Significance was set at p<0.05.
RESULTS
Participants
A total of 3,300 women and 42,281 visits met inclusion criteria (Table 1). Due to missing non-ARV medication data, 661 visits (487 HIV+; 74 HIV−) were excluded from analyses. Of the 3,300 women, 2,328 (71%) women were HIV+. Among HIV+ women, ≥95% adherence to cART was common (82% of visits), viral load was undetectable at 50% of visits, 41% had a history of clinical AIDS, and the lowest median CD4 count at any WIHS visit was 200 cells/mm3 (interquartile range 87–319). Missing data for each variable comprised <10% of visits with the exception of income (11% HIV+; 13% HIV−).
Table 1.
Demographic characteristics of WIHS study participants from 2004–2014
| 1a. Characteristics of individual women | ||
|---|---|---|
| HIV-infected | HIV-uninfected | |
| N= 2328 women | N= 972 women | |
| Race, n (%) | ||
| White* | 300 (12.9) | 102 (10.5) |
| Black* | 1419 (60.9) | 624 (64.2) |
| Hispanic | 534 (22.9) | 207 (21.3) |
| Other* | 75 (3.2) | 39 (4.0) |
| Completed high school, n (%) | 1462 (62.8) | 657 (67.6) |
| Enrollment site, n (%) | ||
| Bronx, NY | 344 (14.8) | 162 (16.7) |
| Brooklyn, NY | 345 (14.8) | 138 (14.2) |
| Chicago, IL | 313 (13.5) | 102 (10.5) |
| Washington, DC | 309 (13.3) | 126 (13.0) |
| San Franciso, CA | 327 (14.0) | 140 (14.4) |
| Los Angeles, CA | 373 (16.0) | 131 (13.5) |
| Miami, FL | 35 (1.5) | 36 (3.7) |
| Atlanta, GA | 90 (3.9) | 73 (7.5) |
| Birmingham, GA | 57 (2.4) | 23 (2.4) |
| Jackson, MS | 52 (2.2) | 16 (1.6) |
| Chapel Hill, NC | 83 (3.6) | 25 (2.6) |
|
| ||
| 1b. Characteristics of women at study visits | ||
| HIV-infected | HIV-uninfected | |
| N= 29800 visits | N= 12481 visits | |
|
| ||
| Age, mean ± SD | 46.0 ± 9.0 | 42.8 ± 10.4 |
| Age, median (IQR) | 46.0 (39.7–52.0) | 42.8 (34.9–50.1) |
| Homeless, n (%) | 544 (1.8) | 406 (3.3) |
| Annual household income > $12,000, n (%) | 13130 (44.1) | 5847 (46.8) |
| Signficant depressive symptoms, n (%) | 9363 (31.4) | 3472 (27.8) |
| Alcohol use per week, n (%) | ||
| Abstain | 16377 (55.0) | 5105 (40.9) |
| 1–7 drinks/week | 8887 (29.8) | 4401 (35.3) |
| 8–12 drinks/week | 940 (3.2) | 661 (5.3) |
| >12 drinks/week | 1476 (5.0) | 1262 (10.1) |
| Recreational drug use, current, n (%) | ||
| Non-injection | 5246 (17.6) | 3327 (26.7) |
| Injection | 363 (1.2) | 238 (1.9) |
Percentages reflect number of women (1a) or visits (1b) with a particular characteristic out of all women (1a) or all visits (1b) by HIV status.
=non-Hispanic.
IQR= interquartile range. SD= standard deviation.
Patterns of NC-AE medication use
Overall, HIV+ women reported NC-AE medication use more often than HIV− women (42% of visits vs. 30%). When all visits were considered (NC-AE medication use and non-use visits), HIV+ women reported greater use of anxiolytic, opioid, antihistamine, gastrointestinal (e.g., loperamide), and antidepressant NC-AE medications versus HIV− women (p’s<0.001; Table 2).
Table 2.
HIV status as a predictor for NC-AE medication use by medication class
| Medication Class | HIV-infected | HIV-uninfected | OR (95% CI) | p-value |
|---|---|---|---|---|
| n visits (%) | n visits (%) | |||
| Anticonvulsant | 1274 (4.3) | 450 (3.6) | 0.96 (0.74–1.24) | 0.74 |
| Antianxiety | 3706 (12.4) | 1047 (8.4) | 1.41 (1.17–1.70) | 0.0004 |
| Anticholinergic | 676 (2.3) | 218 (1.7) | 1.20 (0.86–1.67) | 0.29 |
| Antipsychotic | 2074 (7.0) | 903 (7.2) | 0.93 (0.76–1.15) | 0.52 |
| Amphetamine | 78 (0.3) | 34 (0.3) | 0.79 (0.28–2.20) | 0.66 |
| Opioid | 3420 (11.5) | 1102 (8.8) | 1.35 (1.15–1.60) | 0.0003 |
| Beta Blocker | 1004 (3.4) | 304 (2.4) | 1.29 (0.90–1.86) | 0.17 |
| Gastrointestinal | 807 (2.7) | 186 (1.5) | 1.78 (1.27–2.50) | 0.0009 |
| Antihistamine | 2053 (6.9) | 645 (5.2) | 1.42 (1.17–1.73) | 0.0004 |
| Muscle Relaxant | 718 (2.4) | 316 (2.5) | 0.87 (0.66–1.16) | 0.35 |
| Antidepressant | 6231 (20.9) | 1539 (12.3) | 1.58 (1.35–1.85) | <0.0001 |
HIV-uninfected visits were used as the reference group. OR= odds ratio. CI= confidence interval.
Predictors of NC-AE medication use
In unadjusted models, HIV+ women were more likely to use NC-AE medications compared to uninfected women (p<0.0001; Table 3). A history of clinical AIDS was also a predictor of NC-AE medication use among HIV+ women (p<0.0001). After adjusting for HIV-serostatus, predictors of NC-AE medication use included: having a third-party health payer, elevated depressive symptoms, and non-injection RDU (p’s<0.01). Annual household income >$12,000 and non-hazardous alcohol consumption (1–7 and 8–12 drinks/week) were associated with being less likely to report NC-AE medication use (p’s<0.05).
Table 3.
Predictors of NC-AE medication use at WIHS visit
| Predictors | NC-AE medication use/visits | (%) | OR (95% CI) | p-value | Adjusted for HIV status
|
||
|---|---|---|---|---|---|---|---|
| aOR (95% CI) | p-value | ||||||
| HIV-infected | No | 3739/12481 | (30.0) | 1.52 (1.35–1.71) | <0.0001 | ||
| Yes | 12422/29800 | (41.7) | |||||
|
| |||||||
| Third-party health payer | No | 764/3842 | (19.9) | 1.55 (1.43–1.68) | <0.0001 | 1.54 (1.41–1.67) | <0.0001 |
| Yes | 14332/35435 | (40.4) | |||||
|
| |||||||
| Annual household income >$12,000 | No | 8462/18409 | (46.0) | 0.84 (0.79–0.88) | <0.0001 | 0.83 (0.79–0.88) | <0.0001 |
| Yes | 5902/18977 | (31.1) | |||||
|
| |||||||
| Homeless | No | 14573/38327 | (38.0) | 1.14 (0.99–1.31) | 0.079 | 1.15 (0.99–1.33) | 0.062 |
| Yes | 525/950 | (55.3) | |||||
|
| |||||||
| Completed high school | No | 5963/15294 | (39.0) | 0.92 (0.82–1.03) | 0.15 | 0.94 (0.84–1.05) | 0.25 |
| Yes | 10168/26881 | (37.8) | |||||
|
| |||||||
| Elevated depressive symptoms | No | 8005/25974 | (30.8) | 1.27 (1.21–1.33) | <0.0001 | 1.27 (1.21–1.33) | <0.0001 |
| Yes | 6825/12835 | (53.2) | |||||
|
| |||||||
| Injection RDU | No | 14666/38493 | (38.1) | 0.98 (0.77–1.25) | 0.85 | 0.99 (0.77–1.26) | 0.91 |
| Yes | 324/601 | (53.9) | |||||
|
| |||||||
| Non-injection RDU | No | 10909/30525 | (35.7) | 1.10 (1.02–1.17) | 0.008 | 1.11 (1.03–1.19) | 0.0036 |
| Yes | 4082/8573 | (47.6) | |||||
|
| |||||||
| Alcohol use⋄ | No | 8607/21482 | (40.1) | Reference | Reference | ||
| 1–7 drinks/week | Yes | 4664/13288 | (35.1) | 0.92 (0.87–0.97) | 0.0009 | 0.92 (0.88–0.97) | 0.0015 |
| 8–12 drinks/week | Yes | 589/1601 | (36.8) | 0.89 (0.81–0.98) | 0.021 | 0.90 (0.81–0.99) | 0.034 |
| >12 drinks/week | Yes | 1138/2738 | (41.6) | 0.95 (0.86–1.04) | 0.25 | 0.96 (0.87–1.05) | 0.37 |
|
| |||||||
| Clinical AIDS §* | No | 6153/17844 | (34.5) | 1.71 (1.48–1.97) | <0.0001 | ||
| Yes | 6269/11956 | (54.2) | |||||
NC-AE medication use at visit was used as the dependent (outcome) variable. Logistic regression analysis with exchangeable within-woman correlation was performed on all observations (visits) for all individuals. Odds ratios are shown with and without adjusting for HIV-positive status. RDU= recreational drug use. OR= odds ratio. aOR= adjusted odds ratio. CI= confidence interval.
= No alcohol use (abstain) was used as the reference for the other alcohol use categories.
= Visits at or subsequent to AIDS diagnosis.
= Analysis conducted on HIV-infected individuals only.
NC-AE medication use and HIV-related treatment outcomes
NC-AE medication use at a WIHS visit was a significant predictor of cART use and having an undetectable viral load at that visit (p’s<0.001; Table 4). However, a significant association of NC-AE medication use with cART adherence was not found (p=0.45).
Table 4.
NC-AE medication use as a predictor for HIV treatment outcomes
| Outcome | NC-AE medication use | Outcome/visits | (%) | OR (95% CI) | p-value |
|---|---|---|---|---|---|
| Reported cART use | No | 12441/17375 | (71.6) | 1.46 (1.35–1.57) | <0.0001 |
| Yes | 9731/12422 | (78.3) | |||
|
| |||||
| Reported adherence ≥95%* | No | 9210/11185 | (82.3) | 1.03 (0.95–1.12) | 0.45 |
| Yes | 9072/10947 | (82.9) | |||
|
| |||||
| Undetectable viral load§ | No | 8753/15998 | (54.7) | 1.12 (1.05–1.19) | 0.0008 |
| Yes | 6277/11350 | (55.3) | |||
NC-AE medication use was used as the independent (predictor) variable for this analysis. Logistic regression analysis with exchangeable within-woman correlation was performed on all observations (visits) for all HIV-infected individuals. No NC-AE medication use was used as the reference.
= reported adherence to cART therapy in six months preceding visit.
= both cART and non-CART visits included in the analysis.
OR= odds ratio. CI= confidence interval. cART= combination antiretroviral therapy.
DISCUSSION
To our knowledge, this is the first study to examine patterns and predictors of use of non-ARV medications with known neurocognitive effects, as well as the impact on HIV-related treatment outcomes. HIV+ women were more likely to report using any NC-AE medications compared to HIV− women, specifically antianxiety, opioid, gastrointestinal (primarily antidiarrheal agents), antihistamines and antidepressant medications, all of which are commonly prescribed in HIV practices.26–28 Thus, the differential use of non-ARV NC-AE medications could explain some of the variability in neurocognitive performance in studies examining HIV-associated cognitive impairment.
HIV+ NC-AE medication users in the current study were more likely to use cART than non-NC-AE medication users. This finding is consistent with a previous WIHS study where non-ARV medication users, specifically antidepressant users, were more likely to use cART.29 These findings suggest that use of one prescription medication likely predicts use of other prescribed medications and that use of NC-AE medications appears to occur in the context of ongoing medical care. WIHS is a long-term cohort study during which participants engage with staff who encourage linkages to care, particularly when severe symptoms or lab abnormalities are present; thus, the participants analyzed in this study may be more likely to enter care than individuals who do not participate in a study similar to the WIHS. Receipt of these treatments requires either financial ability to self-pay or health plan coverage for medication costs. The latter is in accordance with our findings that having a health plan predicted NC-AE medication use. However, higher annual incomes (>$12,000) were associated with no NC-AE medication use in our study. While incomes above $12,000 per year might be expected to result in greater ability to pay for medications, higher incomes are likely the result of employment which may be associated with lower mental illness and thus, lower need for use of many of the medications with NC-AE classification.
Abstaining from alcohol use was associated with greater likelihood of NC-AE medication use in our study. This finding could be explained by the recommended avoidance of alcohol with many of the NC-AE medications (e.g., zolpidem, opioids, benzodiazepines) and/or the result of alcohol consumption replacing the need for NC-AE medication use. Recent injection RDU was not common in the cohort, and thus our study had a wide confidence interval for its relation to use of NC-AE medications. However, non-injection RDU was associated with NC-AE medication use, perhaps the result of the widely recognized association between mental illness and RDU or influenced by the inclusion of opioids in the NC-AE category as opioids can be both prescribed and abused for recreational purposes including self-medication for symptom management.30
While the literature regarding the link between non-ARV medication use and neurocognitive impairment in HIV+ individuals is limited; medical conditions that warrant the use of NC-AE medications have been linked to HIV-associated cognitive impairment as well as poor health outcomes. For example, depression, RDU, and stress-disorders are all associated with cognitive vulnerabilities in HIV+ individuals.31–36 Psychiatric disorders and serious mental illness are also associated with poor HIV outcomes.37–41 Additionally, high concomitant medication use may be risk factors for cognitive decline and increased mortality in HIV-uninfected adults.18,42–44 While the directionality of these findings are unknown, it is important to consider concomitant medication use, especially in the case of HIV infection where individuals automatically acquire at minimum three medications upon diagnosis for HIV treatment alone.44–47 Future studies evaluating the relationship between non-ARV medication use and cognitive function in individuals living with HIV should also control for psychiatric comorbidities and sociodemographic factors (e.g., RDU).
We predicted that NC-AE medication use would be associated with worse HIV-treatment outcomes (e.g., high viral load) secondary to potential medication effects on cognition. In contrast, NC-AE medication use was associated with beneficial HIV outcomes. These results may be, at least in part, the result of medical referrals made at the time of WIHS visits or treatment of mental illness. Again, causality cannot be determined given that the data were analyzed retrospectively and relied on self-reported medication use. It is possible that cognitive deficits could have reduced recall of medications used, particularly among HIV+ participants, and thus our findings may be a conservative estimate of the relationship between NC-AE medication use and HIV infection. Investigation of the cognitive burden of NC-AE medication use is currently underway and is an important next step to better understand the best strategies to manage complex HIV-specific and non-HIV associated morbidities.
CONCLUSION
The use of NC-AE medications in women living with HIV is high and more common than in women without HIV infection. NC-AE medication use also appears to be associated with cART use and viral suppression in HIV+ women. The causal direction of these associations remains unclear. Further research is needed to determine if NC-AE medication use exacerbates neurocognitive impairment or if discontinuation of NC-AE medications in cognitively impaired HIV+ individuals leads to improved function. Nonetheless, results from this work further the understanding of non-ARV medication use patterns among HIV+ women. The benefits and harms of NC-AE medications are important clinical considerations for the treatment of comorbid conditions in HIV+ individuals.
Supplementary Material
Acknowledgments
The authors would like to acknowledge Bani Tamraz, PharmD, PhD and Jennifer Cocohoba, MS, PharmD for their contributions in the study design and initial data analysis. We also thank the WIHS participants whose contributions to WIHS made this study possible.
Source of Funding: Data in this manuscript were collected by the Women’s Interagency HIV Study (WIHS). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). WIHS (Principal Investigators): UAB-MS WIHS (Mirjam-Colette Kempf and Deborah Konkle-Parker), U01-AI-103401; Atlanta WIHS (Ighovwerha Ofotokun and Gina Wingood), U01-AI-103408; Bronx WIHS (Kathryn Anastos), U01-AI-035004; Brooklyn WIHS (Howard Minkoff and Deborah Gustafson), U01-AI-031834; Chicago WIHS (Mardge Cohen and Audrey French), U01-AI-034993; Metropolitan Washington WIHS (Seble Kassaye), U01-AI-034994; Miami WIHS (Margaret Fischl and Lisa Metsch), U01-AI-103397; UNC WIHS (Adaora Adimora), U01-AI-103390; Connie Wofsy Women’s HIV Study, Northern California (Ruth Greenblatt, Bradley Aouizerat, and Phyllis Tien), U01-AI-034989; WIHS Data Management and Analysis Center (Stephen Gange and Elizabeth Golub), U01-AI-042590; Southern California WIHS (Joel Milam), U01-HD-032632 (WIHS I – WIHS IV). The WIHS is funded primarily by the National Institute of Allergy and Infectious Diseases (NIAID), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), the National Cancer Institute (NCI), the National Institute on Drug Abuse (NIDA), and the National Institute on Mental Health (NIMH). Targeted supplemental funding for specific projects is also provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute on Alcohol Abuse and Alcoholism (NIAAA), the National Institute on Deafness and other Communication Disorders (NIDCD), and the NIH Office of Research on Women’s Health. WIHS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR000454 (Atlanta CTSA), and P30-AI-050410 (UNC CFAR). Dr. Rubin’s effort was supported by Grant Number 1K01MH098798-01 from the National Institute of Mental Health (NIMH).
Footnotes
Conflicts of Interest: No conflicts of interest were reported by any of the authors.
- Supplemental Digital Content 1, Table, “NCAE_Supplemental table.doc”
Contributor Information
Kendra K. Radtke, Department of Clinical Pharmacy, University of California San Francisco School of Pharmacy, San Francisco, CA.
Peter Bacchetti, Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA.
Kathryn Anastos, Department of Epidemiology & Population Health, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY.
Daniel Merenstein, Department of Family Medicine, Georgetown University Medical Center, Washington, DC.
Howard Crystal, Department of Neurology, State University of New York - Downstate Medical Center, Brooklyn, NY.
Roksana Karim, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA.
Kathleen M. Weber, Cook County Health & Hospital System/Hektoen Institute of Medicine, Chicago, IL.
Andrew Edmonds, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Anandi N. Sheth, Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA.
Margaret A. Fischl, Division of Infectious Diseases, Miller School of Medicine, University of Miami, Miami, FL.
David Vance, School of Nursing, The University of Alabama at Birmingham, Birmingham, AL.
Ruth M. Greenblatt, Department of Clinical Pharmacy, University of California San Francisco School of Pharmacy, San Francisco, CA.
Leah H. Rubin, Department of Neurology, Johns Hopkins University, Baltimore, MD.
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