Abstract
Objective
Recruiting and retaining high-risk individuals is critical for HIV prevention trials.
Design
The current analyses addressed predictors of trial dropout among high-risk HIV-infected men and women.
Results
Trial dropouts (n=74) were more likely to be younger, depressed, and not taking antiretroviral therapy than those who continued (n=815). No other background, substance use, or transmission risk differences were found, suggesting no dropout bias on key risk outcomes.
Conclusions
Efforts are warranted for early detection and treatment of depression and for improving retention of younger participants.
Keywords: Clinical Trials, Prevention, Retention, Depression
Background
Recruiting and retaining high risk participants is critical for the implementation of behavioral trials in HIV primary and secondary prevention (1-4). Attrition or dropout of participants threatens internal and external validity and, if attrition is associated with levels of risk, can impact study outcomes (5).
Purpose
The current analyses explore predictors of attrition in the Healthy Living Project randomized controlled trial of HIV-infected persons at risk for sexual transmission of HIV in four US cities that resulted in an overall decrease in HIV transmission risk (6). In this report, we explore whether differences exist between those who failed to return past randomization compared with those who remained in this successful trial on multiple factors measured at baseline, including demographics, clinical status, depression, substance use, and level of transmission risk.
Methods
Details of trial procedures are provided elsewhere (6-8). 3808 individuals were screened with 936 meeting inclusion criteria enrolled in the trial: risk for transmitting HIV to uninfected persons through unprotected sexual activity. In-person assessments were conducted at baseline and every five months for 25 months (total of 6 assessment points). Approximately half 467 (49.9%) were randomized to the immediate intervention of 15 individual 90 minute sessions and the other 469 (50.1%) were assigned to wait-list control/ lagged intervention. Standardized retention procedures were implemented across sites, including monthly phone calls, mailings, and in-person tracking through contacts identified at baseline.
Assessments included psychosocial (e.g., self-reported depressive symptoms on the Beck Depression Inventory), treatment (e.g., CD4, viral load, receipt of antiretroviral therapy), demographic (race, ethnicity, age, homelessness), and behavioral variables including substance use, number and serostatus of sexual partners, and transmission risk acts (unprotected vaginal or anal intercourse with HIV negative or unknown status partners).
Data analysis
A dropout was defined as a participant who did not return past the baseline assessment at which he/she was randomized. Participants who died (n=47) were not included in analysis. Of the remaining 889 participants, 74 (8.3%) did not return past the first assessment and are defined as dropouts. Bivariate logistic regressions of dropout (0 = no; 1 = yes) on explanatory variables were conducted; explanatory variables with p < .25 were retained for backward elimination multivariate logistic regression analysis (9). In multivariate analysis, explanatory variables with p < .05 were retained. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the overall fit of the final model.
Results
Baseline characteristics are provided in Table 1. Multivariate analyses revealed that dropouts were more likely to be younger, depressed, and less likely to be taking antiretroviral therapy (Table 2). There were no differences based on gender, race/ethnicity, clinical status, antiretroviral adherence, housing status, substance use, or level of HIV transmission risk behavior. The Hosmer-Lemeshow goodness-of-fit test showed excellent fit for this model (χ2 (5) = 1.58, p = 0.90).
Table 1.
Baseline Characteristics
| Whole Sample (n=889) | Lost After 1st Interview (n=74) | |
|---|---|---|
| n (%) | n (%) | |
| Race | ||
| White | 292 (32.9) | 21 (28.4) |
| Latino | 123 (13.9) | 18 (24.3) |
| Black/AA | 401 (45.2) | 29 (39.2) |
| Other |
71 (8.0) |
6 (8.1) |
| Age mean (SD) |
40.3 (7.4) |
37.8 (7.7) |
| Female Gender |
185 (20.8) |
14 (18.9) |
| HS Graduate or more |
717 (80.7) |
57 (77.0) |
| Employed |
331 (37.3) |
32 (43.2) |
| Years Since HIV+ Diagnosis | ||
| 0−7 years | 372 (41.8) | 36 (48.7) |
| 7−13 years | 359 (40.4) | 29 (39.2) |
| 13+ years |
158 (17.8) |
9 (12.2) |
| Biomarker CD4 mean (SD) |
433.1 (274.3) |
458.6 (350.8) |
| Detectable viral load |
549 (63.8) |
52 (74.3) |
| Lagged Randomization status |
451 (50.7) |
29 (39.2) |
| Recent homelessness |
202 (22.8) |
26 (35.1) |
| Lifetime homelessness |
419 (47.2) |
35 (47.3) |
| Antiretroviral Therapy (ARV) Use |
620 (69.8) |
41 (55.4) |
| 100% adherent to ARV medication |
248 (27.9) |
17 (23.0) |
| BDI score range | ||
| Minimal | 509 (57.3) | 33 (44.6) |
| Mild | 161 (18.1) | 14 (18.9) |
| Moderate | 143 (16.1) | 11 (14.9) |
| Severe |
75 (8.5) |
16 (21.6) |
| Site χ2 (DF) | ||
| Los Angeles | 316 (35.6) | 21 (28.4) |
| Milwaukee | 82 (9.2) | 12 (16.2) |
| New York | 228 (25.7) | 17 (23.0) |
| San Francisco |
263 (29.6) |
24 (32.4) |
| Transmission Risk Acts | ||
| 0 | 234 (26.4) | 16 (21.6) |
| 1−5 | 381 (43.1) | 33 (44.6) |
| 6−10 | 127 (14.4) | 13 (17.6) |
| 11−20 | 69 (7.8) | 6 (8.1) |
| 21+ |
74 (8.4) |
6 (8.1) |
| 2+ HIV−/Unknown Partners |
497 (55.9) |
45 (60.8) |
| Any IDU (past year) |
125 (14.1) |
12 (16.2) |
| Alcohol Frequency |
|
|
| None | 288 (32.5) | 21 (28.8) |
| < 4−6 times/week | 537 (60.7) | 46 (63.0) |
| >= 4−6 times/week |
60 (6.9) |
6 (8.2) |
| Marijuana Frequency | ||
| None | 456 (51.4) | 30 (40.5) |
| < 4−6 times/week | 295 (33.2) | 32 (43.2) |
| >= 4−6 times/week |
137 (15.4) |
12 (16.2) |
| Drugs Frequency | ||
| None | 323 (36.5) | 26 (35.1) |
| < 4−6 times/week | 396 (44.8) | 32 (43.2) |
| >= 4−6 times/week |
165 (19.0) |
16 (21.6) |
| Lifetime Drug Seriousness | ||
| Low — Marijuana/alcohol only | 98 (11.2) | 9 (12.5) |
| Med — Other drugs, no IDU | 40 (4.6) | 4 (5.6) |
| High — Hard drugs or IDU | 739 (84.3) | 59 (81.9) |
Note: † Drug frequency includes the use of any of the following: cocaine, crack, speedball, MDMA, opiates, methamphetamine, heroin, methadone, inhalants, stimulants, ketamine, GHB, hallucinogens, sedatives, barbiturates, and steroids.
Table 2.
Predictors of drop-out
| N | Bivariate Odds Ratio (95% CI) | Multivariate Odds Ratio (95% CI) n = 887 | |
|---|---|---|---|
| Race χ2 (DF) | 887 | 6.5 (3) * | — |
| White | — | — | |
| Latino | 2.2 (1.1, 4.3) ** | — | |
| Black/AA | 1.0 (0.6, 1.8) | — | |
| Other |
|
1.2 (0.5, 3.1) |
— |
| Median Age or older |
889 |
0.5 (0.3, 0.9) ** |
0.6 (0.4, 0.98) ** |
| Female Gender |
889 |
0.9 (0.5, 1.6) |
— |
| HS Graduate or more |
889 |
0.8 (0.4, 1.4) |
— |
| Employed |
887 |
1.3 (0.8, 2.1) |
— |
| Years Since HIV+ Diagnosis χ2 (DF) | 889 | 2.5 (2) | — |
| 0−7 years | — | — | |
| 7−13 years | 0.8 (0.5, 1.4) | — | |
| 13+ years |
|
0.6 (0.3, 1.2) * |
— |
| CD4 |
808 |
1.0 (1.0, 1.0) |
— |
| Detectable viral load |
860 |
1.7 (1.0, 3.0) * |
— |
| Lagged randomization status |
889 |
0.6 (0.4, 1.0) ** |
— |
| Recent homelessness |
888 |
2.0 (1.2, 3.3) *** |
— |
| Lifetime homelessness |
888 |
1.0 (0.6, 1.6) |
— |
| Antiretroviral Therapy (ARV) Use |
888 |
0.5 (0.3, 0.8) *** |
0.6 (0.3, 0.9) ** |
| 100% adherent to ARV medication |
620 |
1.1 (0.6, 2.0) |
— |
| BDI score range χ2 (DF) | 888 | 14.6 (3) *** | 12.6 (3) *** |
| Minimal | — | — | |
| Mild | 1.4 (0.7, 2.6) | 1.3 (0.7, 2.5) | |
| Moderate | 1.2 (0.6, 2.4) | 1.2 (0.6, 2.5) | |
| Severe |
|
3.9 (2.0, 7.5) **** |
3.6 (1.8, 7.0) **** |
| Site χ2 (DF) | 889 | 5.2 (3) * | — |
| Los Angeles | — | — | |
| Milwaukee | 2.4 (1.1, 5.1) ** | — | |
| New York | 1.1 (0.6, 2.2) | — | |
| San Francisco |
|
1.4 (0.8, 2.6) |
— |
| Transmission Risk Acts χ2 (DF) | 885 | 1.4 (4) | — |
| 0 | — | — | |
| 1−5 | 1.3 (0.7, 2.4) | — | |
| 6−10 | 1.6 (0.7, 3.3) | — | |
| 11−20 | 1.3 (0.5, 3.5) | — | |
| 21+ |
1.2 (0.5, 3.2) |
— |
|
| 2+ HIV−/Unknown Partners |
889 |
1.2 (0.8, 2.0) |
— |
| Any IDU (past year) |
887 |
1.2 (0.6, 2.3) |
— |
| Alcohol Frequency χ2 (DF) |
885 |
0.7 (2) |
— |
| None | — | — | |
| < 4−6 times/week | 1.2 (0.7, 2.0) | — | |
| >= 4−6 times/week |
|
1.4 (0.5, 3.7) |
— |
| Marijuana Frequency χ2 (DF) | 888 | 4.3 (2) * | — |
| None | — | — | |
| < 4−6 times/week | 1.7 (1.0, 2.9) ** | — | |
| >= 4−6 times/week |
|
1.4 (0.7, 2.7) |
— |
| Drugs Frequency χ2 (DF) | 884 | 0.5 (2) | — |
| None | — | — | |
| < 4−6 times/week | 1.0 (0.6, 1.7) | — | |
| >= 4−6 times/week |
|
1.2 (0.6, 2.4) |
— |
| Lifetime Drug Seriousness χ2 (DF) | 877 | 0.3 (2) | — |
| Low — Marijuana/alcohol only | — | — | |
| Med — Other drugs, no IDU | 1.1 (0.3, 3.8) | — | |
| High — Hard drugs or IDU | 0.9 (0.4, 1.8) | — |
Note
p < 0.25
p < 0.05
p < 0.01
p< 0.001.
For multi-category explanatory variables, multi-parameter Wald chi-square tests, degrees of freedom, and p-values are reported; the first category listed is the reference group.
Discussion
The strongest predictor of dropout was a baseline level of self-reported depressive symptoms consistent with severe depression (10). Given the higher likelihood of mortality and treatment non-adherence associated with depression (11, 12) and the higher rates of dropout found in this and other health-related programs (13), early screening for depression in clinical trials is warranted and can provide opportunity for immediate treatment referral and follow-up. While good clinical research practices include maximizing retention in clinical trials, and exclusion of individuals with depression may help meet retention goals, in the absence of safety concerns, fairness considerations would suggest inclusion of such individuals because of the potential for benefit to the individuals in studies like this one. Moreover, inclusion of individuals with depression, to the extent to which they are present in the population of persons living with HIV/AIDS, increases generalizability findings. Thus, the benefit in terms of knowledge, protection and fair treatment of human participants would outweigh the potential advantages to research design.
That younger age was predictive of dropout is not surprising given the documented challenges of recruiting and retaining younger participants in research studies (13-21). The link between ART receipt and better retention suggests that individuals receiving stable ongoing medical care may have less chaotic personal circumstances. It is also possible that routine medical appointments associated with ART delivery may facilitate adherence to ancillary services.
Dropout was unrelated to level of transmission risk at baseline and to randomization status. This finding provides evidence against the possible effects of selective dropout bias on primary risk study outcomes. If those who dropped out were at higher risk for transmitting HIV and this were related to randomization status (e.g., feeling discouraged by the demands of the intervention), there would be concerns regarding the trial outcomes. Instead, the current analyses provide evidence of the feasibility of retaining high-risk participants in behavioral intervention trials of public health significance.
Limitations of note in the current analyses include the use of a convenience, non-probability-based sample and the use of self-reported data for several key variables, including depression and substance use.
In summary, self-reported depressive symptoms, younger age, and non-receipt of antiretroviral therapy were predictive of attrition in a trial of high-risk HIV-infected men and women. Drug use and HIV transmission risk were unrelated to dropout, supporting the focus on high-risk individuals for risk-reduction interventions.
References
- 1.Anastasi JK, Capili B, Kim GH, Chung A. Clinical trial recruitment and retention of a vulnerable population: HIV patients with chronic diarrhea. Gastroenterol Nurs. 2005;28(6):463–8. doi: 10.1097/00001610-200511000-00002. [DOI] [PubMed] [Google Scholar]
- 2.McQuiston C, Uribe L. Latino recruitment and retention strategies: community-based HIV prevention. J Immigr Health. 2001;3(2):97–105. doi: 10.1023/A:1009565900783. [DOI] [PubMed] [Google Scholar]
- 3.Calsyn RJ, Klinkenberg WD, Morse GA, Miller J, Cruthis R. Recruitment, engagement, and retention of people living with HIV and co-occurring mental health and substance use disorders. AIDS Care. 2004;16(Suppl 1):S56–70. doi: 10.1080/09540120412331315286. [DOI] [PubMed] [Google Scholar]
- 4.Dodds S, Blakley T, Lizzotte JM, Friedman LB, Shaw K, Martinez J, et al. Retention, adherence, and compliance: special needs of HIV-infected adolescent girls and young women. J Adolesc Health. 2003;33(2 Suppl):39–45. doi: 10.1016/s1054-139x(03)00157-5. [DOI] [PubMed] [Google Scholar]
- 5.Hogan JW, Roy J, Korkontzelou C. Handling drop-out in longitudinal studies. Stat Med. 2004;23(9):1455–97. doi: 10.1002/sim.1728. [DOI] [PubMed] [Google Scholar]
- 6.HLP Effects of a Behavioral Intervention to Reduce Risk of Transmission Among People Living With HIV: The Healthy Living Project Randomized Controlled Study. J Acquir Immune Defic Syndr. 2007;44(2):213–221. doi: 10.1097/QAI.0b013e31802c0cae. [DOI] [PubMed] [Google Scholar]
- 7.Gore-Felton C, Rotheram-Borus MJ, Kelly JA, Weinhardt LS, Catz SL, Chesney MA, et al. Healthy Living Project: Individually-Tailored Multidimensional Intervention for HIV-Infected Persons. AIDS Education and Prevention. 2005;17(SA):21–39. doi: 10.1521/aeap.17.2.21.58691. [DOI] [PubMed] [Google Scholar]
- 8.Johnson MO, Catz SL, Remien RH, Rotheram-Borus MJ, Morin SF, Charlebois ED, et al. Theory guided, empirically supported avenues for intervention on HIV medication nonadherence: Findings from the Healthy Living Project. AIDS Patient Care STDS. 2003;17(12):645–656. doi: 10.1089/108729103771928708. [DOI] [PubMed] [Google Scholar]
- 9.Hosmer DW, Lemeshow S. Applied Logistic Regression. John Wiley and Sons; New York, New York: 1989. [Google Scholar]
- 10.Beck AT, Steer RA, Garbin MG. Psychomertric Properties of the Beck Depression Inventory: Twenty-Five Years of Evaluation. Clinical Psychology Review. 1988;8:77–100. [Google Scholar]
- 11.Lima VD, Geller J, Bangsberg DR, Patterson TL, Daniel M, Kerr T, et al. The effect of adherence on the association between depressive symptoms and mortality among HIV-infected individuals first initiating HAART. Aids. 2007;21(9):1175–1183. doi: 10.1097/QAD.0b013e32811ebf57. [DOI] [PubMed] [Google Scholar]
- 12.Mykletun A, Bjerkeset O, Dewey M, Prince M, Overland S, Stewart R. Anxiety, depression, and cause-specific mortality: the HUNT study. Psychosom Med. 2007;69(4):323–31. doi: 10.1097/PSY.0b013e31803cb862. [DOI] [PubMed] [Google Scholar]
- 13.Yohannes AM, Yalfani A, Doherty P, Bundy C. Predictors of drop-out from an outpatient cardiac rehabilitation programme. Clin Rehabil. 2007;21(3):222–9. doi: 10.1177/0269215506070771. [DOI] [PubMed] [Google Scholar]
- 14.Orellana ER, Picciano JF, Roffman RA, Swanson F, Kalichman SC. Correlates of nonparticipation in an HIV prevention program for MSM. AIDS Educ Prev. 2006;18(4):348–61. doi: 10.1521/aeap.2006.18.4.348. [DOI] [PubMed] [Google Scholar]
- 15.Diviak KR, Curry SJ, Emery SL, Mermelstein RJ. Human participants challenges in youth tobacco cessation research: researchers' perspectives. Ethics Behav. 2004;14(4):321–34. doi: 10.1207/s15327019eb1404_4. [DOI] [PubMed] [Google Scholar]
- 16.Kaiser MM, Hays BJ. Recruiting and enrolling pregnant adolescents for research. Issues Compr Pediatr Nurs. 2006;29(1):45–52. doi: 10.1080/01460860500523764. [DOI] [PubMed] [Google Scholar]
- 17.Diviak KR, Wahl SK, O'Keefe JJ, Mermelstein RJ, Flay BR. Recruitment and retention of adolescents in a smoking trajectory study: who participates and lessons learned. Subst Use Misuse. 2006;41(2):175–82. doi: 10.1080/10826080500391704. [DOI] [PubMed] [Google Scholar]
- 18.Hinshaw SP, Hoagwood K, Jensen PS, Kratochvil C, Bickman L, Clarke G, et al. AACAP 2001 research forum: challenges and recommendations regarding recruitment and retention of participants in research investigations. J Am Acad Child Adolesc Psychiatry. 2004;43(8):1037–45. doi: 10.1097/01.chi.0000129222.89433.3d. [DOI] [PubMed] [Google Scholar]
- 19.Stanford PD, Monte DA, Briggs FM, Flynn PM, Tanney M, Ellenberg JH, et al. Recruitment and retention of adolescent participants in HIV research: findings from the REACH (Reaching for Excellence in Adolescent Care and Health) Project. J Adolesc Health. 2003;32(3):192–203. doi: 10.1016/s1054-139x(02)00392-0. [DOI] [PubMed] [Google Scholar]
- 20.Jones FC, Broome ME. Focus groups with African American adolescents: enhancing recruitment and retention in intervention studies. J Pediatr Nurs. 2001;16(2):88–96. doi: 10.1053/jpdn.2001.23151. [DOI] [PubMed] [Google Scholar]
- 21.DiFranceisco W, Kelly JA, Sikkema KJ, Somlai AM, Murphy DA, Stevenson LY. Differences between completers and early dropouts from 2 HIV intervention trials: a health belief approach to understanding prevention program attrition. Am J Public Health. 1998;88(7):1068–73. doi: 10.2105/ajph.88.7.1068. [DOI] [PMC free article] [PubMed] [Google Scholar]