Abstract
The study examines the reliability and validity of a 3-item self-report adherence measure among people with HIV (PWH) experiencing homelessness, substance use, and mental health disorders. 336 participants were included from nine sites across the US between September 2013 and February 2017. We assessed the validity of a self-report scale for adherence to antiretroviral therapy by comparing it with viral load (VL) abstracted from medical records at baseline, 6, 12, and 18 months. The items had high internal consistency (Cronbach’s alpha coefficients at each time point were > 0.8). The adherence scale scores were higher in the group that achieved VL suppression compared to the group that did not. The c-statistic for the receiver-operating characteristic curves pooled across time points was 0.77 for each adherence sub-item and 0.78 for the overall score. The self-report adherence measure shows good internal consistency and validity that correlated with VL suppression in homeless populations.
INTRODUCTION
Despite the widespread use of simpler antiretroviral therapy (ART) with once daily and single pill regimens, only 62% of people with HIV (PWH) achieve 90% adherence worldwide (1). The adherence rate varies across different studies, ranging from 27% to 80% depending on the population studied and the adherence measurement used (2, 3). Poor adherence is associated with myriad complications, both for individual patients, who may experience increased hospitalizations or treatment failure, and for health systems, that must bear the burden in terms of cost and increased risk of HIV transmission. There are several interventions proven to be effective in increasing adherence to ART and subsequently improving clinical outcomes, such as adherence counselling, a once-daily regimen, and text messaging support (4). Among PWH who experience homelessness and drug use, access to addiction treatment, methadone clinics, and housing improved retention in care and adherence (5, 6). Thus, it is vital for practitioners to quickly and accurately assess adherence rates.
Currently, there is no gold standard method for measuring adherence rates in routine clinical care. Therapeutic drug monitoring of ART is not recommended for routine use because it is expensive, not widely available, and the results are difficult to interpret (7). Bottles and pill dispensers equipped with electronic monitors have been used in research studies and record the exact time when the pill container is opened. While they provide an objective method to quantify adherence in PWH, they are not feasible in most clinical settings (8). There are several limitations to the use of electronic drug monitoring: malfunction of the recording system (9), data management issues, and correlation between opening the container cap and the actual ingestion of the prescribed dose of tablets. Some reasons for correspondence failures include participants inadvertently opening the bottle, taking medication from another supply, or “pocket dosing” referring to opening the bottle once for more than one dose and pocketing the extra doses for later use if they were going out, being on a trip, or working (10). Another important limitation is the influence of electronic monitoring on patient’s adherence behavior, positively or negatively (11, 12). In addition, the cost of electronic monitoring is not covered by insurance which limits the routine use in clinical setting. Other studies have used pharmacy refill records, which only indicate pill possession rather than ingestion, and are unavailable in many settings. Unannounced pill counts have been used in research settings but are not practical for large scale routine use. Finally, providers’ assessment of their patients’ adherence with ART has been shown to be inaccurate in previous studies, missing opportunities to intervene to improve ART adherence (8, 13). In a sample of patients with HIV experiencing homelessness, providers overestimated adherence compared to pill counts. Provider estimates for detecting non-adherence were inaccurate, had sensitivity of 40% and a negative predictive value of 53%. One possible explanation is that patients are reluctant to disclose poor adherence to their health care providers (8). In another study, physicians misjudged the degree of adherence for HIV medications in 41% of their patients, while clinic nurses predicted it incorrectly for 30% of patients. The difference between physicians and nurses was not statically significant (odds ratio, 1.8 [CI, 0.8 to 4.0]; P = 0.12) (14).
Self-report adherence measures are the most commonly used methods for quantifying adherence in PWH. They are generally simple, inexpensive and rapid, yet reliable (15), though they generally overestimate adherence. Although several self-report measures have been developed (16–18), few have been rigorously tested to ensure that the questions are understandable and elicit relevant data. The 3-item self-report adherence measure by Wilson et al. has been validated among PWH to assess adherence for both HIV and non-HIV medications. When compared to electronic drug monitoring, the 3-item scale only slightly overestimated adherence (15).
Adherence rates are strongly influenced by multiple psychological factors and can be particularly low among patients diagnosed with substance use, mental health disorders, or experiencing homelessness (19–21). Few self-report adherence measurements have been validated in these key populations who are at high risk for non-adherence (16). The study examines the reliability and validity of a simple 3-item self-report adherence measure among PWH experiencing homelessness, substance use, and mental health disorders.
METHODS
Study design and patient population
We conducted cross-sectional analyses of paired adherence scores and HIV RNA viral load (VL) test results at baseline, 6, 12, and 18 months post study enrollment using data from the Health Resources & Services Administration Special Projects of National Significance (SPNS) Initiative “Building a medical Home for multiply diagnosed HIV homeless populations”. Between September 2013 and February 2017, participants were prospectively enrolled into the initiative from nine sites across the US, including two federally qualified community health centers in Dunn, NC and San Diego, CA; three public health departments in San Francisco, CA, Pasadena, CA, and Portland, OR; one comprehensive HIV/AIDS service organization in Dallas, TX; and three outpatient clinics in Houston, TX, Jacksonville, FL, and New Haven, CT. All nine sites were Ryan White Comprehensive Care agencies. Participants were included if they met all the following inclusion criteria: (1) age 18 years or older, (2) confirmed HIV infection, (3) diagnosed with substance use and/or mental health disorders, and (4) homeless or unstably housed. HIV positivity was determined based on a documented positive HIV antibody test in the patient’s medical record. Eligibility based on housing status was in accordance with the definition for homeless/unstable housing from the U.S. Department of Housing and Urban Development (22). Homeless was defined as lacking a fixed, regular, and adequate nighttime residence. Unstably housed was defined as not having a lease, ownership interest, or occupancy agreement in permanent and stable housing with appropriate utilities (e.g. running water, electricity), or as experiencing persistent housing instability as measured by two moves or more during the preceding 60 days and expected to continue in such status for an extended period. Enrolled participants were followed prospectively at baseline, 6, 12, and 18 months post-enrollment via interviewer-administered surveys and medical record reviews. For interviews, standardized guides were developed, and members were recruited from the care team including behavioral health practitioner, standard-care HIV case manager, primary care provider, programmatic or administrative staff, and SPNS navigator. Further details of the study design are published elsewhere (23).
For inclusion in this secondary analysis, participants had to have been prescribed antiretroviral therapy (ART) at any point in the 12 months prior to any baseline, 6, 12, or 18-month interview and had to have had an HIV VL test done within two weeks of the corresponding interview indicated in the medical record. Only those who self-reported they were prescribed ART were included in the analysis. ART prescription was also verified by medical record review. HIV VL tests were ordered at the discretion of the primary care physician, with VL obtained every 1 to 2 months for persons initiating or changing ART, and every 3 to 6 months for persons stable on ART, consistent with treatment guidelines followed by Ryan White clinics (24). Participants provided an informed written consent for participation. The Institutional Review Boards approved the study at each of the participating sites.
Measures
Interview assessments at baseline, 6, 12, and 18 months were used to collect demographic and ART adherence data. Participant demographic information including age, gender, sexual orientation, race/ethnicity, highest level of education, health insurance, country of birth, primary language, housing status, and number of years homeless was provided by self-report (see Table I for response levels). Two measures were used to assess history of substance use: A dichotomized version of the Individual Score for the WHO Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) using the median score of 26 as the cutoff, and whether or not the participant ever had a history of injection drug use at baseline (Yes, ever; No, never). Depression was assessed using the Center for Epidemiologic Studies Depression Scale (CES-D 10), with a score of 10 or more indicating moderate to severe depression.
Table I.
Baseline characteristics of all participants included in the analysis (n=336)
| Age, yrs mean (SD) | 43.8 (10.4) |
|
| |
| Age groups, years | |
| ≤ 30 | 51 (15.2%) |
| 31 – 54 | 243 (72.3%) |
| ≥ 55 | 42 (12.5%) |
|
| |
| Sex | |
| Male | 255 (75.9%) |
| Female | 69 (20.5%) |
| Transgender or other | 12 (3.6%) |
|
| |
| Sexual identity | |
| Heterosexual | 174 (52.4%) |
| Other | 158 (47.6%) |
|
| |
| Race/Ethnicity | |
| Non-Hispanic Black | 168 (50.1%) |
| Hispanic | 74 (22.1%) |
| Non-Hispanic White | 72 (21.5%) |
| Other and multiracial | 21 (6.3%) |
|
| |
| Place of birth | |
| USA and territories | 311 (92.6%) |
| Other country | 25 (7.4%) |
|
| |
| Primary language | |
| English | 300 (89.3%) |
| Spanish or other | 36 (10.7%) |
|
| |
| Survey administered in Spanish | 22 (6.5%) |
|
| |
| Highest education level attained | |
| Less than high school | 107 (31.8%) |
| High school diploma | 119 (35.4%) |
| Beyond high school | 110 (32.7%) |
|
| |
| Health insurance | |
| Yes | 199 (59.6%) |
| No | 135 (40.4%) |
|
| |
| Housing status in past 12 months | |
| Homeless | 233 (69.3%) |
| Unstably housed | 103 (30.7%) |
|
| |
| Number of Years Homeless | |
| Mean (SD) | 5.8 (7.4) |
Abbreviations: SD, standard deviation
Self-report medication adherence assessments were also done at baseline, 6, 12, and 18-month interviews for each participant at all sites. We used the self-report adherence measure developed by Wilson et al (15). The measure is calculated using three questions: (1) Days taken: In the last 30 days on how many days did you miss at least one dose of any of your [drug name] (write in number of days, 0 – 30; calculated as 30 minus the number of days missed); (2) Frequency: In the last 30 days how often did you take your [drug name] in the way you were supposed to? (never/rarely/sometimes/usually/almost always/always); (3) Rating: In the last 30 days, how good a job did you do at taking your [drug name] in the way you were supposed to? (very poor/poor/fair/good/very good/excellent). Patients were not asked about name of each pill separately, but rather asked each of the items referring to HIV medications. Using methods described and validated by Wilson et al., each questionnaire item was linearly transformed. Days taken calculated as a percentage: (30 - number of days missed) divided by 30. Frequency: scoring ‘always’ = 100, ‘almost always’ = 80; ‘usually’= 60; sometimes’, ‘rarely’ and ‘never’= 30. Rating: scoring ‘excellent’= 100; ‘very good’= 80; ‘good’=60; ‘fair’=40; ‘poor’, ‘very poor’= 10. Summary scales were calculated as the mean of the three individual items, with higher score indicating higher adherence (15).
Medical record reviews at 6, 12, and 18-months post-enrollment collected data on HIV primary care visits and viral load values at each time point.
Data analysis
Proportions with counts and means with standard deviations were computed to describe demographic and background characteristics. We evaluated the internal consistency of the adherence scale using Cronbach’s alpha. Cronbach’s alpha is an estimate of the correlation between two random samples of items in a scale and it is found to be an appropriate index of equivalence. An acceptable level of internal reliability is 0.7 (25). We assessed the criterion validity of the scale at each time point by comparing its results with VL results. A higher adherence score in combination with an undetectable (< 200 copies per mL) VL test served as evidence of a valid adherence scale. We constructed receiver-operating characteristic (ROC) curves pooled across time points for each adherence sub-item and the overall score. The concordance (C)-statistic is a measure of goodness of fit for binary outcomes in a logistic regression model. It is equal to the area under the Receiver Operating Characteristic (ROC) curve and ranges from 0.5 to 1. C-statistics were calculated using PROC SURVEYLOGISTIC in SAS 9.4, adjusting for interview time point and clustering on unique survey respondents. In order to define a clinically relevant cutoff, we divided the adherence score into quartiles and calculated the percentage of participants with viral load suppression.
RESULTS
Participants
Between September 2013 and February 2017, 909 participants were enrolled in the multisite study. Participants who were enrolled but excluded from this analysis (n=573; 63%) were similar in age, gender, place of birth, primary language, sexual orientation, education level, and number of years homeless compared to patients who were included in the analysis. The reasons enrolled persons were not included in the analysis were (1) the participant did not have an ART prescription within 1 year prior to any interview start date, or (2) the viral load test for a given time point was not within 14 days of the corresponding interview date. As shown in Table I, 336 participants were included in the analysis with complete adherence assessment and VL measures at any single time point. The mean age was 43.8 years, 21% were female, and 50% were non-Hispanic black. The majority of the participants were born in the US, spoke English as a primary language, and completed the survey in English.
The average years since HIV diagnosis was 11.5 years (SD 8.8, n = 331) and 30 (9%, n = 331) participants were newly diagnosed with HIV in the 6 months prior to enrollment; 161 (52%, n = 312) participants had a suppressed HIV VL at baseline, 77%, 75%, and 79% were suppressed at 6, 12, and 18 months respectively. A high percentage of participants (70%, n = 235 out 335) screened positive for moderate to severe depression at baseline. Finally, 53% (n = 144 out of 274) reported using illicit drugs excluding marijuana in the last 3 months, and 29% (n = 97 out of 333) reported history of intravenous drug use.
Adherence score by time point and viral load suppression status
Since adherence and outcomes were assessed at multiple time points, we had 431 separate observations for the 336 participants included in this analysis. The individual sub-item self-report adherence score and the 3-item adherence scale mean scores were higher in the group with VL suppression compared to the group without it at baseline (80.6, SD=24.1 vs. 48.9, SD=35.3), 6 months (84.1, SD=19.9 vs. 66.6, SD=28.6), 12 months (88.4, SD=15.8 vs. 62.3, SD=29.5), and 18 months (90.6, SD=14.3 vs. 68.7, SD=29.1) (Table II). When divided into quartiles, 74% of scores in the lowest quartile, correlated to an adherence score of less than 70, were associated with detectable HIV VL and 26% had undetectable HIV VL. In contrast, an adherence score of more than 70 was associated with an almost equal distribution of detectable and undetectable HIV VL (Table III).
Table II.
The individual sub-item self-report adherence score and the 3-item adherence scale mean score overall by timepoint and viral load suppression status (n=431)
| Overall | Detectable viral load | Undetectable viral load | |||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Mean (SD) | Median | Mean (SD) | Median | Mean (SD) | Median | ||
|
|
|||||||
| Baseline (n= 48, 11%) | Days taken | 72.0 (41.0) | 96.7 | 50.9 (48.6) | 70.0 | 89.9 (22.9) | 100.0 |
| Frequency | 69.4 (30.3) | 60.0 | 55.4 (31.1) | 25.0 | 81.1 (24.5) | 80.0 | |
| Rating | 56.9 (36.3) | 80.0 | 40.4 (33.7) | 30.0 | 70.8 (32.8) | 100.0 | |
| 3-item scale | 66.1 (33.5) | 74.5 | 48.9 (35.3) | 55.0 | 80.6 (24.1) | 88.9 | |
|
| |||||||
| 6-months (n= 160, 37%) | Days taken | 88.3 (23.9) | 100.0 | 75.0 (33.3) | 93.3 | 91.9 (19.2) | 100.0 |
| Frequency | 81.0 (23.8) | 80.0 | 69.7 (28.3) | 60.0 | 84.0 (21.5) | 80.0 | |
| Rating | 71.8 (29.3) | 80.0 | 55.0 (33.8) | 80.0 | 76.3 (26.3) | 100.0 | |
| 3-item scale | 80.4 (23.1) | 86.7 | 66.6 (28.6) | 72.2 | 84.1 (19.9) | 86.7 | |
|
| |||||||
| 12-months (n= 142, 33%) | Days taken | 88.3 (24.5) | 98.4 | 69.6 (39.1) | 90.0 | 94.6 (11.7) | 100.0 |
| Frequency | 82.7 (23.1) | 80.0 | 62.8 (26.9) | 60.0 | 89.4 (17.2) | 100.0 | |
| Rating | 74.4 (28.7) | 100.0 | 54.4 (31.6) | 60.0 | 81.2 (24.3) | 100.0 | |
| 3-item scale | 81.8 (23.0) | 86.7 | 62.3 (29.5) | 71.7 | 88.4 (15.8) | 93.3 | |
|
| |||||||
| 18-months (n= 81, 19%) | Days taken | 92.9 (17.6) | 100.0 | 74.7 (34.3) | 86.7 | 97.0 (5.8) | 100.0 |
| Frequency | 87.3 (22.7) | 100.0 | 71.3 (31.4) | 80.0 | 90.9 (18.8) | 100.0 | |
| Rating | 79.4 (26.8) | 100.0 | 60.0 (35.0) | 80.0 | 83.8 (22.7) | 100.0 | |
| 3-item scale | 86.5 (19.7) | 97.8 | 68.7 (29.1) | 81.1 | 90.6 (14.3) | 98.9 | |
Abbreviations: SD, standard deviation
Since adherence and outcomes were assessed at multiple time points, we have 431 separate observations for the 336 participants included in this analysis
Table III.
The 3-item adherence scale mean score overall divided into quartiles by viral load suppression status (n=431)
| Adherence scale mean score | Detectable viral load n (%) |
Undetectable viral load n (%) |
|---|---|---|
| 0 – 69 | 68 (73.9) | 24 (26.1) |
| 70 – 84 | 58 (58.0) | 42 (42.0) |
| 85 – 99 | 50 (53.2) | 44 (46.8) |
| 100 | 77 (53.1) | 68 (46.9) |
Since adherence and outcomes were assessed at multiple time points, we have 431 separate observations for the 336 participants included in this analysis
Reliability and Validity
The Cronbach’s alpha coefficients at each time point were 0.91 at baseline, 0.87, 0.88 and 0.84 at 6, 12, and 18 months respectively. The overall c-statistic for the 3-item scale ROC curve predicting undetectable VL, adjusting for time point and clustering on participant ID, was 0.76. For percent days taken it was 0.728, for percent rating it was 0.739, and for percent frequency it was 0.730. When divided into sub-groups by each of homeless status, depression score, history of intravenous drug use, and ASSIST Substance Use Score, the c-statistics for the ROC curves predicting VL suppression were ≥ 0.7 for all the adherence sub-items and the 3-item adherence overall score (Table IV).
Table IV.
C-statistics for raw self-report items and 3-item scale predicting undetectable viral load status (n=431).
| n (%) | 3-Item Scale | % Days Taken | % Rating | % Frequency | |
|---|---|---|---|---|---|
|
| |||||
| Overall | |||||
| VL undetectable | 324 (75%) | 0.760 | 0.728 | 0.739 | 0.730 |
| VL detectable | 107 (25%) | ||||
|
| |||||
| Baseline Housing Status | |||||
| Homeless (n = 292) | |||||
| VL undetectable | 216 (74%) | 0.783 | 0.768 | 0.765 | 0.744 |
| VL detectable | 76 (26%) | ||||
| Unstably Housed (n = 139) | |||||
| VL undetectable | 108 (78%) | 0.695 | 0.668 | 0.667 | 0.700 |
| VL detectable | 31 (22%) | ||||
|
| |||||
| Baseline CES-D Score | |||||
| CES-D Score < 10 (n = 127) | |||||
| VL undetectable | 100 (79%) | 0.787 | 0.779 | 0.754 | 0.763 |
| VL detectable | 27 (21%) | ||||
| CES-D Score ≥ 10 (n = 303) | |||||
| VL undetectable | 223 (74%) | 0.747 | 0.728 | 0.733 | 0.722 |
| VL detectable | 80 (26%) | ||||
|
| |||||
| Baseline Injection Drug Use, Ever | |||||
| No, never (n = 306) | |||||
| VL undetectable | 229 (75%) | 0.783 | 0.744 | 0.757 | 0.745 |
| VL detectable | 77 (25%) | ||||
| Yes, ever (n = 121) | |||||
| VL undetectable | 92 (76%) | 0.710 | 0.751 | 0.704 | 0.700 |
| VL detectable | 29 (24%) | ||||
|
| |||||
| Baseline ASSIST Substance Use Score, including Alcohol and Tobacco, Median cut-off: 26 | |||||
| < Median (n = 211) | |||||
| VL undetectable | 161 (76%) | 0.800 | 0.718 | 0.767 | 0.759 |
| VL detectable | 50 (24%) | ||||
| ≥ Median (n = 220) | |||||
| VL undetectable | 163 (74%) | 0.741 | 0.761 | 0.728 | 0.721 |
| VL detectable | 57 (26%) | ||||
C-statistics were calculated using PROC SURVEYLOGISTIC in SAS 9.4, adjusting for interview time point and clustering on unique survey respondents.
Abbreviations: CES-D: Center for Epidemiologic Studies Depression Scale; VL, viral load;
Since adherence and outcomes were assessed at multiple time points, we have 431 separate observations for the 336 participants included in this analysis
DISCUSSION
When monitoring adherence to medication in routine care, it is essential to rapidly identify non-adherent patients, establish the causes of non-adherence, and address those causes with effective interventions. The ability of practitioners to perform this monitoring requires the development, use and validation of an easy and reliable adherence measurement tool. In this study, we conducted a cross-sectional analysis of self-reported adherence and VL suppression among PWH experiencing homelessness, substance use, and mental health disorders. We used the 3-item self-report adherence measure initially developed and validated by Wilson et al. and assessed its validity in PWH at high risk of non-adherence. We found that this measure demonstrated good internal consistency and criterion validity that correlated with HIV VL suppression. These reliability and validity measures remained strong in sub-groups by each of homeless status, depression, and substance use disorders.
Our investigation was performed across nine different sites across the US, demonstrating that the adherence measure has application in multiple settings. Our study also demonstrates the adherence scale’s validity in patients who are experiencing homelessness with substance use and mental health disorders, populations which may experience high rates of non-adherence and arguably benefit most from brief, reliable adherence assessments during time-constrained clinical encounters.
In addition, our results demonstrate that this tool can help to identify the population at risk for not achieving viral suppression. An important challenge in ART adherence research is that a well-defined level of adherence to maintain virologic suppression on current ART regimens remains unclear (26). Earlier studies suggested that high adherence rate of 90 to 95% is necessary to achieve virologic suppression in most patients Ref, however, recent studies have challenged that concept (27). The level of adherence required for virologic suppression varied between studies for several reasons, the definition of virologic suppression applied, the methodologies used for measuring adherence, the patient population (ARV-naïve patients vs. experienced), the pharmacokinetics and half-life of different ART have varied among the different studies, making it different to compare (28). A threshold of 70 for the 3-item adherence scale mean scores could be helpful when attempting to establish which patients might fail to adhere to the treatment. This population could be targeted for additional treatment adherence support such as counselling, regimen simplification, and easy access to substance use and mental health treatment (5, 29) to promote suppression and reduce the risk of HIV transmission.
Our study has several limitations. Patients were asked about their HIV medications as a regimen. They were not asked about individual medication names, which could be a single pill regimen or more than one pill regimen. Patients could be referring to one or more antiretroviral drug. We did not have information on the ART regimen prescribed to each patient, and different ART formulations may have different minimum cutoff levels for adherence needed to achieve HIV VL suppression. In addition, previous studies have shown that interviewer-administered interviews may receive different responses compared to computer-collected interviews due to expectations created through voice intonation, body language, facial expressions, and subjective bias, especially when reporting behaviors that are socially undesirable such as medication non-adherence. Third we limited our sample to those participants with complete data at each time point. Our attrition analysis did not find significant differences with those lost to care, we recognized that our findings may be biased to those who are engaged in care.
In conclusion, this brief 3-item self-report adherence measure shows good internal consistency and predictive validity that correlated with HIV VL suppression in homeless population. This measure provides a time-saving, inexpensive adherence assessment tool for clinicians and researchers working with PWH experiencing mental health, substance use disorders, and homelessness.
Acknowledgements
This project was supported by the Health Resources and Services Administration (HRSA) of the US Department of Health and Human Services (HHS) under grant U90HA24974, Special Projects of National Significance Initiative “Building a Medical Home for Multiply Diagnosed HIV-Positive Homeless Populations.
Footnotes
Conflict of interest: The authors declare that they have no conflicts of interest.
Compliance with ethical standards
Ethical approval: All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent: Informed consent was obtained from all individual participants included in the study.
References
- 1.Ortego C, Huedo-Medina TB, Llorca J, Sevilla L, Santos P, Rodríguez E, et al. Adherence to highly active antiretroviral therapy (HAART): a meta-analysis. AIDS Behav. 2011;15(7):1381–96. [DOI] [PubMed] [Google Scholar]
- 2.Iacob SA, Iacob DG, Jugulete G. Improving the Adherence to Antiretroviral Therapy, a Difficult but Essential Task for a Successful HIV Treatment-Clinical Points of View and Practical Considerations. Front Pharmacol. 2017;8:831. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Viswanathan S, Detels R, Mehta SH, Macatangay BJ, Kirk GD, Jacobson LP. Level of adherence and HIV RNA suppression in the current era of highly active antiretroviral therapy (HAART). AIDS Behav. 2015;19(4):601–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Mbuagbaw L, Sivaramalingam B, Navarro T, Hobson N, Keepanasseril A, Wilczynski NJ, et al. Interventions for Enhancing Adherence to Antiretroviral Therapy (ART): A Systematic Review of High Quality Studies. AIDS Patient Care STDS. 2015;29(5):248–66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Palepu A, Milloy MJ, Kerr T, Zhang R, Wood E. Homelessness and adherence to antiretroviral therapy among a cohort of HIV-infected injection drug users. J Urban Health. 2011;88(3):545–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kidder DP, Wolitski RJ, Royal S, Aidala A, Courtenay-Quirk C, Holtgrave DR, et al. Access to housing as a structural intervention for homeless and unstably housed people living with HIV: rationale, methods, and implementation of the housing and health study. AIDS Behav. 2007;11(6 Suppl):149–61. [DOI] [PubMed] [Google Scholar]
- 7.Pretorius E, Klinker H, Rosenkranz B. The role of therapeutic drug monitoring in the management of patients with human immunodeficiency virus infection. Ther Drug Monit. 2011;33(3):265–74. [DOI] [PubMed] [Google Scholar]
- 8.Bangsberg DR, Hecht FM, Clague H, Charlebois ED, Ciccarone D, Chesney M, et al. Provider assessment of adherence to HIV antiretroviral therapy. J Acquir Immune Defic Syndr. 2001;26(5):435–42. [DOI] [PubMed] [Google Scholar]
- 9.Denhaerynck K, Schäfer-Keller P, Young J, Steiger J, Bock A, De Geest S. Examining assumptions regarding valid electronic monitoring of medication therapy: development of a validation framework and its application on a European sample of kidney transplant patients. BMC Med Res Methodol. 2008;8:5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Berg KM, Arnsten JH. Practical and conceptual challenges in measuring antiretroviral adherence. J Acquir Immune Defic Syndr. 2006;43 Suppl 1:S79–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487–97. [DOI] [PubMed] [Google Scholar]
- 12.Wagner GJ, Ghosh-Dastidar B. Electronic monitoring: adherence assessment or intervention? HIV Clin Trials. 2002;3(1):45–51. [DOI] [PubMed] [Google Scholar]
- 13.Miller LG, Liu H, Hays RD, Golin CE, Beck CK, Asch SM, et al. How well do clinicians estimate patients’ adherence to combination antiretroviral therapy? J Gen Intern Med. 2002;17(1):1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Paterson DL, Swindells S, Mohr J, Brester M, Vergis EN, Squier C, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med. 2000;133(1):21–30. [DOI] [PubMed] [Google Scholar]
- 15.Wilson IB, Lee Y, Michaud J, Fowler FJ, Rogers WH. Validation of a New Three-Item Self-Report Measure for Medication Adherence. AIDS Behav. 2016;20(11):2700–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Giordano TP, Guzman D, Clark R, Charlebois ED, Bangsberg DR. Measuring adherence to antiretroviral therapy in a diverse population using a visual analogue scale. HIV Clin Trials. 2004;5(2):74–9. [DOI] [PubMed] [Google Scholar]
- 17.Vale FC, Santa-Helena ET, Santos MA, Carvalho WMDE, Menezes PR, Basso CR, et al. Development and validation of the WebAd-Q Questionnaire to monitor adherence to HIV therapy. Rev Saude Publica. 2018;52:62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Chesney MA, Ickovics JR, Chambers DB, Gifford AL, Neidig J, Zwickl B, et al. Self-reported adherence to antiretroviral medications among participants in HIV clinical trials: the AACTG adherence instruments. Patient Care Committee & Adherence Working Group of the Outcomes Committee of the Adult AIDS Clinical Trials Group (AACTG). AIDS Care. 2000;12(3):255–66. [DOI] [PubMed] [Google Scholar]
- 19.Hinkin CH, Barclay TR, Castellon SA, Levine AJ, Durvasula RS, Marion SD, et al. Drug use and medication adherence among HIV-1 infected individuals. AIDS Behav. 2007;11(2):185–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Feelemyer J, Des Jarlais D, Arasteh K, Uusküla A. Adherence to antiretroviral medications among persons who inject drugs in transitional, low and middle income countries: an international systematic review. AIDS Behav. 2015;19(4):575–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Mellins CA, Havens JF, McDonnell C, Lichtenstein C, Uldall K, Chesney M, et al. Adherence to antiretroviral medications and medical care in HIV-infected adults diagnosed with mental and substance abuse disorders. AIDS Care. 2009;21(2):168–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Homeless Emergency Assistance and Rapid Transition to Housing: Defining “homeless.” Department of Housing and Urban Development, Office of the Assistant Secretary for Community Planning and Development, 24 CFR Parts 91, 582, and 583. 2011. [Google Scholar]
- 23.Rajabiun S, Tryon J, Feaster M, Pan A, McKeithan L, Fortu K, et al. The Influence of Housing Status on the HIV Continuum of Care: Results From a Multisite Study of Patient Navigation Models to Build a Medical Home for People Living With HIV Experiencing Homelessness. American Journal of Public Health. 2018;108(Suppl 7):S539–S45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.DHHS Panel on Antiretroviral Guidelines for Adults and Adolescents. Guidelines for the Use of Antiretroviral Agents in HIV-1-Infected Adults and Adolescents. Department of Health and Human Services, ed,. 2011. [Google Scholar]
- 25.Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16(3):297–334. [Google Scholar]
- 26.Gordon LL, Gharibian D, Chong K, Chun H. Comparison of HIV Virologic Failure Rates Between Patients with Variable Adherence to Three Antiretroviral Regimen Types. AIDS Patient Care STDS. 2015;29(7):384–8. [DOI] [PubMed] [Google Scholar]
- 27.Kobin AB, Sheth NU. Levels of adherence required for virologic suppression among newer antiretroviral medications. Ann Pharmacother. 2011;45(3):372–9. [DOI] [PubMed] [Google Scholar]
- 28.Shuter J, Sarlo JA, Kanmaz TJ, Rode RA, Zingman BS. HIV-infected patients receiving lopinavir/ritonavir-based antiretroviral therapy achieve high rates of virologic suppression despite adherence rates less than 95%. J Acquir Immune Defic Syndr. 2007;45(1):4–8. [DOI] [PubMed] [Google Scholar]
- 29.Lucas GM, Chaudhry A, Hsu J, Woodson T, Lau B, Olsen Y, et al. Clinic-based treatment of opioid-dependent HIV-infected patients versus referral to an opioid treatment program: A randomized trial. Ann Intern Med. 2010;152(11):704–11. [DOI] [PMC free article] [PubMed] [Google Scholar]