People with HIV (PWH) have a smoking prevalence of over 40% (Weinberger et al., 2017), at least two times higher than the general population (Asfar et al., 2021; Mdodo et al., 2015). PWH are also less than half as likely to quit smoking (De Socio et al., 2020; Gritz et al., 2004). In the general population, smoking cessation programs tailored to a subpopulation, such as gender and sexual minority (Lee et al., 2014), race/ethnicity (Rusk et al., 2022), age, and smoking-related comorbidity (i.e., cancer, hospitalization, pulmonary disease, cardiovascular disease, or mental health diagnosis), may have higher success than nontailored programs (Hartmann-Boyce et al., 2021; Scheffers-van Schayck et al., 2021). Identifying and tailoring smoking cessation programs for specific at-risk subpopulations among those who have HIV (i.e., at the intersection of HIV and other high-risk subpopulations) may help further decrease disparities in tobacco use and treatment (Moscou-Jackson et al., 2014), but few interventions specific for marginalized or high-risk subpopulations exist among PWH or have been tested in PWH (Mann-Jackson et al., 2019).

In general, people with smoking-related comorbidities are a high-priority subpopulation for tailored smoking cessation interventions. Yet, they may have a more difficult time quitting smoking (Liu et al., 2022; Tonnesen et al., 2022) due to higher nicotine dependence (Jimenez-Ruiz et al., 2001; Shahab et al., 2006), concurrent mental health conditions (Wagena et al., 2005), lower self-efficacy (Crowley et al., 1995; Masefield et al., 2016), limited treatment availability, and poorer treatment response (Rojewski et al., 2016). Although life expectancy for PWH has drastically increased, they are nevertheless at an increased risk for a wide range of comorbidities-some of which are smoking related-perhaps due to HIV medication (Guaraldi et al., 2014), chronic low-grade inflammation (Schouten et al., 2014), longer life expectancies (Marcus et al., 2020), and higher smoking prevalence (Asfar et al., 2021; Johnston et al., 2021; Maggi et al., 2019). PWH not only have a higher risk for comorbidity (Brothers et al., 2014; Ghosn et al., 2018; Guaraldi et al., 2014; Marcus et al., 2020; Raffe et al., 2022) but also a higher number of those comorbidities (Paudel et al., 2022; Schouten et al., 2014), and multimorbidity continues to rise (Guaraldi et al., 2015; Yang et al., 2021). Among PWH, this additional burden of comorbidity may be one of the reasons successful quitting is low. Among people without HIV, smoking cessation programs tailored for those with smoking-related comorbidities, including cardiovascular disease, cancer, pulmonary disease, and mental health diagnoses, have found that some treatments may be more effective than others (Tonnesen et al., 2022) and that tailored smoking cessation programs that consider the unique characteristics of people with these comorbidities may be effective. For example, bupropion has been found to be not as effective among people who experience acute coronary syndrome as in the general population (Eisenberg et al., 2013).

Tailoring smoking cessation programs for PWH who have smoking-related comorbidities may increase quitting success, which may help further decrease disparities in tobacco use and treatment. Understanding the sociodemographic and clinical correlates of these types of comorbidities among PWH who smoke will help to inform clinical care as well as the development of interventions and guidelines for smoking cessation for this subpopulation. We assessed the prevalence and correlates of having a smoking-related physical and/or mental health comorbidity among PWH who smoke using a large prospective observational cohort study of PWH drawn from the Washington, D.C. metropolitan area.

Methods

A cross-sectional analysis was conducted using data from the D.C. Cohort Longitudinal HIV Study ("DC Cohort"). The DC Cohort is a large prospective longitudinal cohort study of PWH living in the Washington, D.C. area. The methodology of the DC Cohort has been previously described (Greenberg et al., 2016). Briefly, participants are recruited from 15 HIV care centers in the Washington, D.C. metropolitan area, and electronic health record data from these clinics are extracted on a monthly basis. All patients with HIV at participating clinics are approached and offered enrollment, which occurs on an ongoing basis; participants were included if they provided consent and excluded if they did not provide consent. Enrollment began in January 2011 and is currently ongoing. This present analysis includes data for participants enrolled from DC Cohort inception (January 2011) to March 2017. Data for all participants in the cohort during this period were included in this analysis. The study protocol was approved by multiple participating Institutional Review Boards, and participants provided informed consent before participation.

Clinical and sociodemographic information, including age, race, gender identity, sexual orientation, socioeconomic conditions, alcohol use, recreational drug use (including heroin, cocaine, crystal meth, ecstasy, or marijuana), and smoking status, were abstracted electronically or entered manually from medical records at the time of enrollment ("baseline"); there was limited manual data entry. Research assistants conducted an electronic health record review and classified subjects' smoking status as current, former, never, or unknown. Smoking was then defined as (a) current, former, never, or unknown/missing, and (b) ever, never, or unknown/missing. We used International Classification of Diseases, Ninth Revision (ICD-9) and ICD-10 codes for history of smoking, current smoking, and medications for smoking cessation treatment, including varenicline and nicotine replacement therapy, to correct for missing information or classification errors (Table 1, Supplemental Digital Content 1, http://links.lww.com/JNC/A40). Codes for smoking-related diagnoses and medications within 6 months before enrollment, or any time after enrollment, were included. Bupropion was not included, given it can also be used to treat depression. Values for HIV clinical characteristics at baseline were included if measured within 6 months of enrollment date.

Comorbid conditions were broadly included as (a) cardiovascular {(acute myocardial infarction, unstable angina, angina pectoris [stable angina], peripheral arterial disease, transient ischemic attack, ischemic stroke, and congestive heart failure)}, (b) cancer (any cancer diagnosis), (c) pulmonary (chronic obstructive pulmonary disease or asthma), and (d) mental health diagnoses (any mood disorder, anxiety or stress/trauma-related disorder, psychotic disorder, or severe mental health disorder), which were categorized with ICD-9 and ICD-10 diagnosis codes at any time point before enrollment and within 6 months after enrollment into the cohort (Table 1, Supplemental Digital Content 1, http://links.lww.com/JNC/A40). These comorbidities were chosen because they are smoking related and result in a large proportion of the burden of morbidity and mortality both in the general population and in PWH (GBD 2019 Diseases and Injuries Collaborators, 2020; Marcus et al., 2020; Mokdad et al., 2018; Paudel et al., 2022). The cardiovascular comorbidities included were selected given that they are acute, advanced stage conditions that are likely to be the most difficult for patients and their clinicians to manage. Further, there is a large literature base for tailored smoking cessation programs for those in the general population with these conditions, which may be readily translated to this population of PWH (Rojewski et al., 2016); understanding the burden of these selected comorbidities and their correlates represents the most actionable subpopulations to intervene on. Participants were then categorized by the number of comorbid categories they fell into, ranging from 0 to 4, with 4 meaning they were coded as having all four categories of comorbidity. We further dichotomized comorbidity type as (a) a mental health comorbidity or (b) a cardiovascular, pulmonary, or cancer (CPC) comorbidity. Although the purpose of this study was to evaluate prevalence and correlates of diagnosis of any of the selected comorbidities among PWH who smoke, given the potential bidirectional models of causation for smoking and mental health comorbidity, as well as the disproportionately large burden of mental health comorbidity compared with CPC morbidity in this population, for this analysis mental health comorbidity was evaluated separately. Furthermore, although it would have been preferable to evaluate CPC diagnoses separately, we did not have enough outcomes of each to do so, and thus, the decision was made to combine these comorbidities into one category.

Statistical Analysis

All subjects 18 years of age or older were included in this analysis. Multiple imputation using the mice package (van Buuren & Groothuis-Oudshoorn, 2011) in R was conducted assuming that data were missing at random. Outcome, sociodemographic, smoking status, and clinical variables were included in the imputation model, with 30 imputations performed. Results from imputed data sets were compared with a complete case analysis to assess the validity of our missingness assumption. Imputed data sets were then restricted to current smokers for our primary analysis. Pooled estimates of the number, prevalence, and corresponding 95% confidence intervals of sociodemographic, smoking, and clinical variables among smokers in the imputed data were evaluated by our comorbidity outcomes using Chi ([chi]²) square, Fisher exact, and Kruskal-Wallis rank sum tests, as appropriate. Pooled univariable and multivariable Poisson regression analysis with robust estimates for the standard error was conducted to estimate the relative risks of association between these correlates of interest and comorbidity. Variables were included in multivariable regression based on a priori hypothesis of association or if found to be associated with the outcome in univariable analysis with p < .10. All analyses were conducted in R (Version 3.5.2; R Core Team, 2018).

Results

Of 8,665 participants in the full cohort, 8,581 (99%) were adults aged 18 years and older and 84 (1%) were adolescents. Among adults who smoke (n = 4,307, 50%), the majority (n = 2,427, 57%) of participants were younger than 50 years, 3,682 (85%) were non-Hispanic Black, and 3,052 (71%) identified as male. Less than a quarter of participants had private health insurance (n = 647, 15%), and 3,288 (76%) were unemployed. Over half (n = 2,346, 54%) of participants were diagnosed with any comorbidity; 159 (4%) with cardiovascular disease, 326 (8%) with cancer, 102 (2%) with pulmonary disease, and 2,097 (49%) with a mental health comorbidity (Table 3, Supplemental Digital Content 3, http://links.lww.com/JNC/A40).

Associations between demographic correlates of interest and comorbidity were found to differ by mental health (Table 1) and CPC (Table 2) outcomes. In adjusted analyses, the estimated risk of having a mental health comorbidity was 20% greater for those aged 50-59 years (prevalence ratio [PR]: 1.19; 95% confidence interval [CI] [1.09-1.31]). Age was more strongly associated with an outcome of a CPC comorbidity, however; the effect size for those aged 50-59 years was 1.97 (95% CI [1.46-2.65]), and risk was also increased for those aged 40-49 years (PR: 1.52; 95% CI [1.12-2.05]) and those older than 60 years (PR: 4.11; 95% CI [3.00-5.63]). Non-Hispanic Black participants had a lower risk for a mental health comorbidity (PR: 0.69; 95% CI [0.62-0.76]), but a nonsignificant higher risk for having a CPC comorbidity (PR: 1.17; 95% CI [0.84-1.62]) as compared with non-Hispanic White participants. Male participants had a lower estimated risk for both a mental health and a CPC comorbidity as compared with female participants; however, the effect size was stronger for a CPC comorbidity (PR: 0.68; 95% CI [0.57-0.81]) than for a mental health comorbidity (PR: 0.88; 95% CI [0.81-0.94]).

Table 1. Demographic and Clinical Characteristics and Correlates [

Table 2. Demographic and Clinical Characteristics and Correlates [

Trends for association with socioeconomic status also varied by outcome of a mental health or CPC comorbidity. For an outcome of a mental health comorbidity, estimated risk was increased for participants with public or no health insurance (PR: 1.43; 95% CI [1.25-1.64]), who fell into the disabled, student, or retired employment category (PR: 1.27; 95% CI [1.05-1.54]), and who do not have permanent housing (PR: 1.17; 95% CI [1.08-1.26]). For a CPC comorbidity, the estimated risk was also higher for those without permanent housing (PR: 1.65, 95% CI [1.33-2.05]), but no difference was seen by the type of insurance or employment status. Non-D.C. residents were at lower risk for a mental health comorbidity (PR: 0.91; 95% CI [0.82-1.00]) but higher risk for a CPC comorbidity (PR: 1.26; 95% CI [1.03-1.55]) as compared with subjects who lived in D.C. Small estimated effect estimates were found for the association between alcohol use and body mass index for both comorbidity categories, which were also not statistically significant.

As compared with those with a nadir CD4⁺ of >500 cells/[micro]l, a nadir CD4⁺ of 200-500 cells/[micro]l (PR: 1.35; 95% CI [1.04-1.76]) and <200 cells/[micro]l (PR: 1.50; 95% CI [1.07-2.10]) was associated with a CPC comorbidity, but nadir CD4⁺ was not associated with a mental health comorbidity. As compared with a baseline CD4⁺ of >500 cells/[micro]l, a baseline CD4⁺ count of 200-500 cells/[micro]l (PR: 0.92; 95% CI [0.85-1.00]) and <200 cells/[micro]l (PR: 0.87; 95% CI [0.76-1.00]) were negatively associated with a mental health comorbidity, but not associated with a CPC comorbidity. A baseline viral load of at least 200 copies/ml was not associated with a mental health comorbidity but was negatively associated with a CPC comorbidity (PR: 0.79; 95% CI [0.63-0.99]) as compared with a baseline viral load of less than 200 copies/ml.

Evaluation of the multiple imputation methodology is presented in the Tables 1-7 (Supplemental Digital Content 1-7, http://links.lww.com/JNC/A40). The largest number of missing observations were found for variables related to housing status, substance use, and employment (Table 2, Supplemental Digital Content 2, http://links.lww.com/JNC/A40). Comparison of the univariate analysis for our imputed results versus the complete case analysis focusing on socioeconomic status and substance use variables, which had the most missing data, showed similar results, with differences in the effect estimates generally less than 0.20 (Tables 3 and 4, Supplemental Digital Content 3 and 4, http://links.lww.com/JNC/A40). For the outcome of a mental health comorbidity, differences in the observed versus imputed values were greatest for substance use variables; however, they were not large in magnitude and estimates were in consistent directions. For the outcome of a CPC comorbidity, differences were greatest for the socioeconomic status variables, but these were also not large and were all in consistent directions. We observed no important differences in individuals with complete versus incomplete data when evaluating the distribution of tabulated results.

Discussion

In this analysis of a large cohort of PWH living in the Washington, D.C. area, over half of all current smokers were diagnosed with any of the selected smoking-related comorbidities. PWH who smoke and who have a mental health comorbidity are more likely to be older, White, female, have public or no health insurance, no work, and have unstable housing conditions. Having a CPC comorbidity is much more strongly associated with older age and is also associated with non-Hispanic Black race/ethnicity. Those with a CPC comorbidity are also more likely to be female and have unstable housing, but CPC was not associated with other sociodemographic variables. Although both types of comorbidity categories were associated with poorer clinical characteristics (i.e., history of an AIDS diagnosis, low CD4⁺ nadir, and high baseline viral load), these were most strongly associated with a CPC comorbidity. This is expected given less favorable clinical characteristics may lead to a higher prevalence of CPC comorbidity. We did not see any association with substance use. These sociodemographic factors and clinical characteristics have been previously associated with difficulty in quitting smoking among the general population and may be informative for clinical care and when developing and implementing smoking cessation strategies for PWH who also have these comorbidities.

The development of effective smoking cessation strategies for PWH who smoke and have concurrent smoking-related comorbidities should be prioritized. In addition to increasing risk for developing a comorbidity, continued smoking poses challenges for treatment and management of that comorbidity. In general, continued smoking has a negative impact on comorbidity symptoms, disease progression, and mortality (U.S. Department of Health and Human Services, 2014). More intensive or targeted smoking cessation interventions may be required for PWH who smoke and have a concurrent smoking-related comorbidity, and strategies should consider the type of comorbidity an individual is managing (Rojewski et al., 2016). Management of these comorbidities may also make it more difficult to maintain cessation. In the general population in the United States, smokers with a comorbidity have a 20% higher likelihood of trying to quit smoking, but these quit attempts are no more successful than smokers without a comorbidity (Kalkhoran et al., 2018). This suggests that perhaps their attempts are less likely to succeed for unknown reasons, even though they are using evidence-based methods for quitting (Kalkhoran et al., 2018). Furthermore, smokers with comorbidities often have a more difficult time quitting due to factors such as higher nicotine dependence (Jimenez-Ruiz et al., 2001, 2015; Nair et al., 2018; Shahab et al., 2006; van Eerd et al., 2015) and less self-efficacy and self-esteem for quitting (Crowley et al., 1995; Jimenez-Ruiz et al., 2015; van Eerd et al., 2015). It is also important to consider therapeutic conflict of best-practice cessation pharmacotherapy and some comorbid conditions because pharmacotherapies for smoking cessation may be contraindicated among individuals with comorbidities. Examples include contraindications of nicotine replacement therapy among people with recent cardiovascular events or varenicline for people with severe mental health conditions.

People with HIV, like other marginalized populations such as Hispanic/Latinx smokers (Medina-Ramirez et al., 2022) or those who are homeless (Pinsker et al., 2018), are not a homogenous group, and understanding subgroup differences will inform targeted smoking cessation interventions. In this population, special consideration of lower socioeconomic status, female gender, and race/ethnicity may be important when developing interventions given our findings of association with concurrent comorbidity. Given the positive association between comorbidities and female gender, interventions for PWH who have comorbidities should consider that women with HIV may have a more difficult time quitting given multiple life stressors (Fletcher et al., 2019), and that stress may increase cravings more among women than men (Erblich et al., 2022). Women may have an even more difficult time quitting if they have lower educational and insurance status (Breger et al., 2022). Considerations of pharmacotherapy or other interventions to manage the complexities of additional stress and higher craving may increase success. Women may also face challenges in quitting due to concerns about weight control, and women who are better able to control their weight during the quitting process may have higher abstinence success (Kuo et al., 2022). Further, in general, women may receive suboptimal HIV care due to psychosocial and structural barriers that disproportionately affect women (Short et al., 2019). Cessation success by race/ethnicity may be due to variations in self-efficacy (Pinsker et al., 2018), amount of cigarettes smoked (Trinidad et al., 2009), or lack of access to resources (Leventhal et al., 2022), among other differences. Although we found that non-Hispanic Black participants in this cohort had a lower risk for a mental health comorbidity, future studies should evaluate whether this is a consistent finding or rather an artifact of a lack of diagnosis due to care-seeking behavior or disparity in diagnosis on the part of the clinician. Among PWH in the United States participating in North American AIDS Cohort Collaboration on Research and Design, PWH who are Black have been shown to be approximately 13% less likely to have a multimorbidity than those who are White, and no difference was found by sex (Wong et al., 2018). In this present study, we found a positive association between CPC comorbidity and non-Hispanic Black race/ethnicity, which may be due to persistent racial disparities and an overall disproportionately high burden of comorbidities in general among Black residents of Washington, D.C. (King & Cloonan, 2020). Our measures of socioeconomic status were limited in scope given the nature of the available data, and only housing and living location were associated with CPC outcomes. Although we anticipated employment and insurance status to also be positively associated with CPC comorbidity, the observed lack of association may be due to imperfect measure of the variable, the variables being poor markers of socioeconomic status in this population, or unassociated due to some unmeasured confounding.

Clinical intervention for smoking cessation is recommended at every encounter for PWH using an advise-connect framework, and system-level intervention has been highlighted as a high-priority area for future research (Clinical Practice Guideline Treating Tobacco Use and Dependence 2008 Update Panel, Liaisons, and Staff, 2008). Importantly, clinical management of PWH with any concurrent comorbidity, smoking related or not, is likely to become more complex, with novel frameworks for integrated care emerging to address multiple comorbidities (Wong et al., 2018). This complexity will pose an additional challenge to developing and integrating smoking cessation interventions within existing care. Given the increased burden of management of multiple conditions, clinicians may experience a decreased capacity to deliver smoking cessation interventions. Grouping of comorbidities may make clinical management more difficult (Fortin et al., 2007; Starfield, 2011) and lead to suboptimal smoking cessation intervention. For example, smokers with increasing clinical complexity, such as concurrent coronary heart disease and mental health conditions, have been shown to be less likely to receive smoking cessation advice as compared with those with coronary heart disease but no mental health conditions (Blane et al., 2017). In addition to medical and psychiatric comorbidities, smokers in lower socioeconomic categories or marginalized racial/ethnic groups may also receive suboptimal cessation services (Liu et al., 2021). Furthermore, the high burden of concurrent mental health conditions may be particularly challenging in achieving smoking cessation. Approaching smoking cessation through the lens of a single disease for PWH with concurrent comorbidities may be inefficient (Wolff et al., 2002) as well as insufficient.

This study has limitations worth noting. First, this is an observational cohort using medical record abstraction, including for smoking status at baseline. No biochemical validation was used to verify smoking status, and as such participants may be misclassified as nonsmokers if they did not accurately disclose their tobacco use to their clinical provider. Further, we only ascertained current, former, and never smoking, but did not distinguish between other types of tobacco, the use of electronic nicotine delivery systems, poly-tobacco use, or the amount or duration of tobacco use. Bupropion was excluded in the definition of smoking-related medication; however, in our data set, no participants had been prescribed bupropion. Comorbid conditions were defined by ICD-9 and ICD-10 codes and diagnosed by clinicians. However, we only included selected comorbidities, and we grouped them in broad categories. Furthermore, we may have defined someone as only having one comorbidity when they may have multiple comorbidities but in a single category. For example, someone diagnosed with two cardiovascular comorbidities would have only been defined as having one comorbidity in this analysis, given they were in the cardiovascular category. Our data show a lower prevalence of comorbidities than many other studies, which may be due to our definitions, specifically excluding hypertension as an outcome for cardiovascular comorbidity. Participants for this study were drawn from an existing cohort that enrolls PWH from the Washington, D.C. metro area, and thus, results may not be fully generalizable to other regions in the United States. Results of this study are most likely generalizable to urban settings in the United States with sociodemographic characteristics and an HIV epidemic most similar to that of Washington, D.C., and may have limited generalizability to more rural settings. We conducted multiple imputation under the assumption that data were missing at random, which is an untestable assumption. We evaluated whether there may be important differences between individuals with complete and incomplete data, and although we observed slight differences for socioeconomic status variables and substance use variables, given the small magnitude of difference and consistency of direction of effect, we deemed multiple imputation appropriate.

Although PWH are disproportionally burdened by tobacco use and subsequent health impacts and are in need of targeted tobacco cessation interventions, PWH with comorbidities may have unique characteristics that should be considered during treatment for both their medical conditions and for smoking cessation interventions (Rojewski et al., 2016). If they have perceived competing health priorities, such as a comorbid condition, this may take away urgency or space for smoking cessation efforts, both through the lens of the health care provider and through the lens of the patient. Cessation strategies may need to include intervention by, and communication between, multiple health care professionals who are providing simultaneous care to be most effective. Strategies to tailor interventions for smokers with comorbidities that are commonly used in the general population should be extended to PWH. Understanding the characteristics of PWH who smoke and have comorbidities, and developing cessation strategies through this lens, is needed to continue to identify the most effective and efficient way to support PWH quit smoking, especially those with a comorbidity, and may be a way to help reduce this persistent health disparity.

Disclosures

The authors report no real or perceived vested interests related to this article that could be construed as a conflict of interest.

Funding

The DC Cohort is funded by the National Institute of Allergy and Infectious Diseases, UM1 AI069503 and 1R24AI152598-01.

Data Availability

Complete data for this analysis cannot be publicly shared because of ethical restrictions. HIV is a highly stigmatized condition, and the dataset includes a population from a limited geographic region (people receiving HIV care in Washington, D.C.). Re-identification of de-identified datasets may be possible when they are combined with publicly available datasets, and the risk of de-identification in this case may be slightly higher because of the limited geographic area. The DC Cohort Collaboration Policies under which the DC Cohort was founded require the submission and approval of a project concept sheet by the DC Cohort Executive Committee, which includes the principal investigators at participating sites. All datasets provided by DC Cohort to individual investigators are de-identified according to HIPAA Safe Harbor guidelines (https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identifica). DC Cohort requires the signing of a Data Use Agreement before HIV clinical data can be released. Instructions for how to obtain DC Cohort data are outlined on the DC Cohort website: https://publichealth.gwu.edu/projects/dc-cohort-longitudinal-hiv-study#datashari.

Author Contributions

All authors on this paper meet the four criteria for authorship as identified by the International Committee of Medical Journal Editors (ICMJE); all authors have contributed to the conception and design of the study, drafted or have been involved in revising this manuscript, reviewed the final version of this manuscript before submission, and agree to be accountable for all aspects of the work. Specifically, using the CRediT taxonomy, the specific contributions of each author is as follows: conceptualization & methodology: J. L. Elf and R. Niaura; funding acquisition: J. L. Elf and R. Niaura; formal analysis: J. L. Elf; project administration: M. E. Levy, A. Castel, and A. Monroe; writing-original draft: J. L. Elf; writing-review & editing: K. Horn, L. Abroms, C. A. Stanton, A. Cohn, F. Spielberg, T. Gray, E. Harvey, C. Debnam, M. E. Levy, A. Castel, A. Monro, and R. Niaura.

Key Considerations

* People with HIV (PWH) who smoke have a high prevalence of comorbidity, especially mental health comorbidity.

* People with HIV who smoke and have a comorbidity are more likely to have socioeconomic characteristics associated with more difficulty quitting than the general population.

* Among PWH who smoke, being female and Black is positively associated with having a comorbidity. These populations may also have unique challenges in quitting smoking, which should be considered when developing targeted smoking cessation strategies.

Acknowledgments

The authors would like to acknowledge and thank the participants of the DC Cohort. Data in this manuscript were collected by the DC Cohort Study Group with investigators and research staff located at: Children's National Hospital Pediatric clinic (Natella Rakhmanina); the Senior Deputy Director of the DC Department of Health HAHSTA (Clover Barnes); Family and Medical Counseling Service (Angela Wood); Georgetown University (Princy Kumar); The George Washington University Biostatistics Center (Marinella Temprosa, Vinay Bhandaru, Tsedenia Bezabeh, Nisha Grover, Lisa Mele, Susan Reamer, Alla Sapozhnikova, and Greg Strylewicz); The George Washington University Department of Epidemiology (Shannon Barth, Morgan Byrne, Amanda Castel, Alan Greenberg, Shannon Hammerlund, Paige Kulie, Anne Monroe, James Peterson, and Bianca Stewart) and Department of Biostatistics and Bioinformatics (Yan Ma); The George Washington University Medical Faculty Associates (Jose Lucar); Howard University Adult Infectious Disease Clinic (Jhansi L. Gajjala) and Pediatric Clinic (Sohail Rana); Kaiser Permanente Mid-Atlantic States (Michael Horberg); La Clinica Del Pueblo (Ricardo Fernandez); MetroHealth (Duane Taylor); Washington Health Institute, formerly Providence Hospital (Jose Bordon); Unity Health Care (Gebeyehu Teferi); Veterans Affairs Medical Center (Debra Benator); Washington Hospital Center (Glenn Wortmann); and Whitman-Walker Institute (Stephen Abbott).

References