Open Access

Repeat HIV-testing is associated with an increase in behavioral risk among men who have sex with men: a cohort study

BMC Medicine201513:218

https://doi.org/10.1186/s12916-015-0458-5

Received: 29 June 2015

Accepted: 19 August 2015

Published: 7 October 2015

Abstract

Background

The Center for Disease Control and Prevention recommends that high-risk groups, like sexually active men who have sex with men (MSM), receive HIV testing and counseling at least annually. The objective of this study was to investigate the relationship between voluntary repeat HIV testing and sexual risk behavior in MSM receiving rapid serologic and nucleic acid amplification testing.

Methods

We performed a cohort study to analyze reported risk behavior among MSM receiving the “Early Test”, a community-based, confidential acute and early HIV infection screening program in San Diego, California, between April 2008 and July 2014. The study included 8,935 MSM receiving 17,333 “Early Tests”. A previously published risk behavior score for HIV acquisition in MSM (i.e. Menza score) was chosen as an outcome to assess associations between risk behaviors and number of repeated tests.

Results

At baseline, repeat-testers (n = 3,202) reported more male partners and more condomless receptive anal intercourse (CRAI) when compared to single-testers (n = 5,405, all P <0.001). In 2,457 repeat testers there was a strong association observed between repeated HIV tests obtained and increased risk behavior, with number of male partners, CRAI with high risk persons, non-injection stimulant drug use, and sexually transmitted infections all increasing between the first and last test. There was also a linear increase of risk (i.e. high Menza scores) with number of tests up to the 17th test. In the multivariable mixed effects model, more HIV tests (OR = 1.18 for each doubling of the number of tests, P <0.001) and younger age (OR = 0.95 per 5-year increase, P = 0.006) had significant associations with high Menza scores.

Conclusions

This study found that the highest risk individuals for acquiring HIV (e.g. candidates for antiretroviral pre-exposure prophylaxis) can be identified by their testing patterns. Future studies should delineate causation versus association to improve prevention messages delivered to repeat testers during HIV testing and counseling sessions.

Keywords

Acute and early HIV MSM Risk behavior NAT screening Repeat testing

Background

Men who have sex with men (MSM) bear the greatest burden of HIV infection in California and the United States [15]. The Center for Disease Control and Prevention recommends that high-risk groups, like sexually active MSM, receive HIV testing and counseling at least annually [6]. To address this recommendation, community-based, confidential HIV screening programs that include screening for acute HIV (i.e. detect HIV antigen in antibody negative persons), like the “Early Test” in San Diego, California [7], have been implemented in many US metropolitan areas. Higher rates of repeat testing and counseling have been reported in persons with more behavioral risks for HIV acquisition [810], though most of these studies were conducted either in resource limited settings or more than 10 years ago (i.e. before the era of rapid HIV antibody or acute HIV infection screening) [1113].

HIV testing and counseling typically includes an assessment of recent behavioral risks, pre-test counseling (in some settings), and rapid provision of HIV test results. Detection of HIV infection is often associated with at least transiently reduced risk behaviors and thus a decreased risk of HIV transmission [14, 15]. When test results are negative, however, the client could interpret this as positive reinforcement that ongoing risk behaviors are not sufficiently risky to result in HIV infection [16]. In other words, reported risk behaviors without significant consequences may foster greater risk in the future [17]. Thus, repeated negative HIV test results over time may provide an unintentionally reassuring message that may ultimately contribute to higher HIV-acquisition rates among repeat testers. In particular, acute infection screening (i.e. nucleic acid amplification testing) provides HIV status information related to very recent risk behaviors as well as greater certainty about a negative HIV test result [18]. We investigated the relationship between voluntary repeat HIV testing and sexual risk behavior in MSM receiving rapid serologic testing and nucleic-acid testing (NAT).

Methods

In this retrospective analysis of a prospective cohort study, we analyzed risk behavior reported for the previous 12 months in individuals who enrolled in the “Early Test” HIV screening program between April 2008 and July 2014. The “Early Test” [19] is a community-based, voluntary, confidential acute and early HIV infection (AEH) screening program that provides point of care rapid HIV serologic testing followed by routine reflex to individual donation HIV NAT (NAT results are provided in a second visit or, if negative, also with automated voice mail or online) in all antibody-negative persons [7, 20, 21]. With the “Early Test” program approximately 4000 individuals per month are screened free of charge at five regular plus additional mobile testing sites (including sites targeting MSM at the Lesbian, Gay, Bisexual, Transgender Center and the Gay Men’s Health Clinic; the San Diego County Health Department; the AntiViral Research Center (AVRC); substance abuse treatment centers; and special community event venues) in San Diego, United States. Similar to other settings in the United States, MSM bear by far the greatest burden of HIV infection in San Diego, as reflected in the “Early Test”, where 72 % of tests overall, 85 % of repeat testing encounters, and 88 % of HIV diagnoses are made to MSM. Since the “Early Test” does not routinely keep track of individuals who repeatedly test over time, tests were considered linked to the same individual if the birth date and soundex of last-name and first-name matched.

Pre-test counseling was provided at each Early Test visit throughout the study period and utilized a client-centered harm reduction model offering personalized risk reduction options. Risk behavior was collected by using a risk assessment form with 19 detailed survey questions (focusing primarily on sexual risk behavior, substance use, sexually transmitted infection (STI) diagnoses – all reported for the prior 12 months – and demographics). Survey questions were assessed and the form filled out by the testing staff before each HIV testing encounter, and data was later entered into the data system (always in duplicate to minimize data entry errors). After testing, clients were recommended to come back 6 months later for the next testing encounter. Males and female-to-male transsexual persons who reported sexual contact with one or more male partners during the previous 12 months were included in the analysis. We excluded (1) those who were diagnosed with HIV (irrespective of HIV stage) at the first test since these participants did not have the opportunity to become repeat testers, and (2) those with only one “Early Test” who had repeat tests required as part of their participation in another study (Fig. 1). AEH was characterized using previously published criteria for serologic and virologic test results [22]. We evaluated the predictive potential (for AEH versus HIV negatives) of a previously published risk behavior score for HIV acquisition in MSM repeat testers [23] as an outcome measure to assess changes in risk behavior. The shorter version of this score (i.e. the Menza score), included four variables: diagnosis or history of STI, use of methamphetamine or inhaled nitrites (i.e. poppers) in the prior 6 months, 10 or more male sex partners in the prior year, and unprotected anal intercourse with someone who is HIV positive in the prior year [23]. Two modifications were necessary for our study: (1) diagnosis or history of STI was modified to self-reported diagnosis of STI within the last 12 months, and (2) time period of methamphetamine or inhaled nitrite use was increased to last 12 months (as we did not have data available for last 6 months). Performance of the Menza score in our cohort was evaluated using receiver operating characteristics (ROC) analysis and area under the curve (AUC) value, including 95 % confidence interval (CI) displayed.
Fig. 1

Tree of enrollment for analyses on baseline risk-taking behavior in single- and repeat-testers and risk behavior increase in repeat-testers (reasons/number of excluded individuals are displayed in grey shapes)

Our main analyses focused on: (1) baseline risk behavior in single- and repeat- testers, and (2) risk behavior over time in repeat testers.

Baseline risk behavior in single and repeat testers

For the analysis of baseline risk behavior, we evaluated the differences in risk behavior reported at the initial HIV screening between single testers and repeat testers. To evaluate the difference between single and repeat testers, we compared the baseline risk-taking behaviors in the 12 months prior to testing using χ2 and Mann Whitney U test. To identify baseline characteristics that were associated with repeat testing, we used Cox proportional-hazards regression, modeling the time from initial test to (1) second test for repeat testers or (2) end of follow-up period for single testers. Univariate analyses were performed, and variables with P <0.20 were included in the multivariable model. Variables in the final model were selected with a forward stepwise procedure. Hazard ratios (HR) including 95 % CI were displayed.

Risk behavior at first and last test in repeat testers

For the comparison of risk behavior reported at the first and last test in repeat testers, those with incomplete basic risk data at their first or last test (i.e. number of male partners, STI and drug use), and those who had their first and last test less than 180 days apart were additionally excluded (as analysis focused on risk behavior reported for the prior 12 months, Fig. 1). Analyses of changes in risk behaviors between the first and the most recent “Early Test” used the McNemar test and Wilcoxon signed rank test.

Risk behavior and number of tests in repeat testers

Further, individuals with repeat “Early Test” visits who received additional protocol-driven interval tests as part of co-enrollment in another study (n = 44) were excluded from the analyses to evaluate risk behavior over time in repeat testers (Fig. 1). Analyses of changes in risk behaviors between the first and the most recent “Early Test” was performed separately for those with 2 to 3, 4 to 5, and 6 or more tests as described above.

Risk behavior at each Early Test visit was further characterized with the previously published Menza score [23]. Since scores clustered around awarded points based on four specific risk behaviors (i.e. weighted scores for risk behavior vary between 1 and 11), and therefore did not follow a discrete-count distribution, we dichotomized scores based on the median: high risk (above the mean of individuals’ median scores) or low risk (below the mean). At each time point, the cumulative number of HIV tests was used as the primary predictor and the effect of the number of tests was investigated while controlling for follow-up time and age. Percentage of high (i.e. above the mean) HIV behavioral risk scores by number of HIV tests was calculated for repeat testers using local regression (Loess), a smoothing technique that allows one to see a pattern without assuming a particular distribution. Visual and Loess fitting suggested that there was a linear relationship between the number of HIV tests and the risk-behavior score up to approximately the 17th test. As subsequent tests (from 18th test onward) did not correspond to the same increase in higher scores, numbers of tests were log-transformed to achieve a linear relationship between number of tests and the logit of high-risk scores.

Univariable and multivariable mixed-effects logistic regression models were performed for number of tests, time since first HIV test and age, and odds ratios (OR) including 95 % CI were displayed. In addition, associations between the number of tests (log2) and individual known risk behaviors for HIV that were not included in the Menza score were assessed by univariable mixed-effects logistic regression models.

For statistical analysis SPSS 21 (SPSS Inc., Chicago, IL, USA) and SAS 9.4 (SAS Institute, Cary, NC) were used.

The UCSD Human Research Protections Program approved the study protocol, consent, and all study related procedures. All study participants provided voluntary, written informed consent before any study procedures were undertaken.

Results

A large population (n = 14,612 unique clients) underwent HIV screening using the “Early Test” between April 2008 and July 2014, including 8,935 (61 %) unique MSM (with 17,333 voluntary HIV tests). Notably, 10 % (n = 892) of MSM participants reported sex with women in the past year. Overall, 419 of 8,935 MSM (4.69 %) were newly diagnosed with HIV infection, 219 (2.45 %) with chronic HIV infection, and 200 (2.24 %) with AEH (125/200 (63 %) at their first “Early Test”, 75/200 (37 %) at a repeated visit). Incidence rate among MSM repeat testers was 1.783 per 100 person years. The Menza score [23] for HIV infection among MSM was significantly higher in those with AEH than in those without HIV (median 3 points, interquartile range (IQR) 0–11 versus median 0 point, IQR 0–4, P <0.001) and displayed a moderate AUC value of 0.636 (95 % CI, 0.594–0.677) for prediction of AEH.

Baseline risk behavior in single and repeat testers

A comparison of baseline risk behaviors in the 12 months prior to testing and demographics between single and repeat testers is shown in Table 1. A total of 5,402 (63 %) single and 3,202 (37 %) repeat testers (Fig. 1) were included in the analysis. Repeat testers had up to 30 HIV tests during a mean period of follow-up of 104 weeks (standard deviation 84 weeks). Approximately 50 % of repeat testers had 2 or 3 tests and another 25 % (n = 814) had 5 or more tests. At baseline, repeat testers reported significantly more male partners and more condomless receptive anal intercourse (CRAI), and fewer reported sexual contacts with females when compared to single testers (all P <0.001).
Table 1

Comparison demographics and risk behavior at baseline. Reported by individuals with a single HIV test during the study period versus those with repeated HIV tests (those with two or more tests, and the subpopulation with five or more tests)

Risk factor/risk behavior (within 12 months prior to test) and demographics

Single testers a

Repeat testers (2 or more tests) a

P value b

Repeat tester (5 or more tests) a

P value b

N

5402

3202

 

814

 

Number of male partners (median, IQR)

5 (3–10)

6 (3–12)

<0.001

6 (4–14)

<0.001

10 or more male partners

1624/5397 (30.1 %)

1120/3197 (35.0 %)

<0.001

319 (39.2 %)

<0.001

Intercourse with females also

594/5389 (11.0 %)

277/3192 (8.7 %)

<0.001

64/813 (7.9 %)

0.007

CIAI with male

3188/5347 (59.6 %)

1974/3170 (62.3 %)

0.016

491/804 (61.1 %)

n.s.

CRAI

2611/5343 (48.9 %)

1664/3167 (52.5 %)

0.001

434/802 (54.1 %)

0.003

CRAI and 5 or more male partners

1544/5342 (28.9 %)

1102/3165 (34.8 %)

<0.001

306/802 (38.2 %)

<0.001

CRAI and 10 or more male partners

892/5342 (16.7 %)

632/3165 (20.0 %)

<0.001

181/802 (22.6 %)

<0.001

CRAI with HIV positive

214/5097 (4.2 %)

121/2947 (4.1 %)

n.s.

19/725 (2.6 %)

0.043

CRAI with PWID

56/5281 (1.1 %)

29/3129 (0.9 %)

n.s.

4/790 (0.5 %)

n.s.

CRAI with sex worker

21/5147 (0.4 %)

9/3052 (0.3 %)

n.s.

1/764 (0.1 %)

n.s.

Worked as sex worker

84/3799 (2.2 %)

45/2004 (2.3 %)

n.s.

8/424 (1.9 %)

n.s.

Syphilis c

101 (1.9 %)

59 (1.8 %)

n.s.

23 (2.8 %)

n.s.

Gonorrhea c

274 (5.1 %)

193 (6.0 %)

n.s.

47 (5.8 %)

n.s.

Chlamydia c

214 (4.0 %)

129 (4.0 %)

n.s.

48 (5.9 %)

0.01

Any STI c

596 (11.0 %)

402 (12.6 %)

0.033

126 (15.5 %)

<0.001

Methamphetamine, not injected

363 (6.7 %)

189 (5.9 %)

n.s.

44 (5.4 %)

n.s.

Non-injection stimulant drug use (i.e. methamphetamine, cocaine, poppers, GHB, ketamine, XTC)

1316 (24.4 %)

765 (23.9 %)

n.s.

176 (21.6 %)

n.s.

IDU

77 (1.4 %)

37 (1.2 %)

n.s.

5 (0.6 %)

n.s.

IDU with shared needles

35/5378 (0.7 %)

7/3180 (0.2 %)

0.006

0

0.021

Demographic data

 Age (years; median, IQR)

33 (26–43)

32 (26–42)

0.011

32 (26–41)

n.s.

 Male

5402 (100 %)

3202 (100 %)

n.s.

814 (100 %)

 

 Hispanic origin

1434/5249 (27.3 %)

850/3105 (27.4 %)

n.s.

203/785 (25.9 %)

n.s.

 Race

<0.001

<0.001

  Caucasian

3513/5066 (69.3 %)

2112/3021 (69.9 %)

n.s.

547/783 (69.9 %)

n.s.

  African-American

317/5066 (6.3 %)

143/3021 (4.7 %)

0.005

39/783 (5.0 %)

n.s.

  Asian

398/5066 (7.9 %)

195/3021 (6.5 %)

0.022

52/783 (6.6 %)

n.s.

  Pacific Islander

142/5066 (2.8 %)

53/3021 (1.8 %)

0.004

11/783 (1.4 %)

0.030

  Native American

37/5066 (0.7 %)

15/3021 (0.5 %)

n.s.

1/783 (0.1 %)

n.s.

  Other

659/5066 (13.0 %)

503/3021 (16.7 %)

<0.001

133/783 (17.0 %)

0.002

CIAI, Condomless insertive anal intercourse; CRAI, Condomless receptive anal intercourse; GHB, Gamma hydroxybutyrate; IDU, Injection drug use; IQR, Inter-quartile range; MSM, Men who have sex with men; n.s., Not significant; PWID, Person who injects drugs; STI, Sexually transmitted infection; XTC, Ecstasy

a Data available from all individuals if denominator not depicted

b Calculated using χ2 or Mann–Whitney U-test

c All self-reported diagnosis within the last 12 months

In the multivariable analysis, CRAI (HR, 1.089; 95 % CI, 1.01–1.173; P = 0.026), 10 or more male partners (HR, 1.189; 95 % CI, 1.1–1.285; P <0.001), and not having sexual contact with females also (HR, 0.824; 95 % CI, 0.725–0.936; P = 0.003) were associated with repeat testing (also included in the model: injection drug use, CRAI with a HIV positive male, and STI).

Risk behavior at first and last test in repeat testers

Risk behaviors reported at the first and last test are summarized in Table 2 for 2,501 repeat testers (Fig. 1) with more than 180 days between their first and last test (median time between first and last test 714 days, IQR, 396–1191). Demographic characteristics of the evaluable 2,501 repeat testers reflected those observed for repeat testers in Table 1. Using Wilcoxon’s signed rank sum test, repeat testers reported significantly more male partners (P = 0.001) and fewer female partners (P <0.001) at the last test when compared to the first. Further, the proportions of individuals reporting 10 or more male partners alone or in combination with CRAI, CRAI with an HIV-positive male, CRAI with a person who injects drugs (PWID), gonorrhea, and non-injection stimulant drug use (i.e. methamphetamine, cocaine, poppers, gamma hydroxybutyrate, ketamine, ecstasy) were significantly higher at the last test when compared to the first (Table 2).
Table 2

Risk behavior at the first and last test in 2,501 individuals undergoing repeat HIV testing a

Risk factor/risk behavior (within 12 months prior to test)

First test

Last test

 

Individuals reporting risk

Percentage

Individuals reporting risk

Percentage

P value using McNemar test

10 or more male partners

871/2501

34.83 %

970/2501

38.78 %

<0.001

CRAI

1308/2474

52.87 %

1323/2490

53.13 %

n.s.

CRAI and 5 or more male partners

859/2483

34.60 %

915/2495

36.67 %

0.028

CRAI and 10 or more male partners

488/2483

19.65 %

580/2495

23.25 %

<0.001

CRAI with HIV positive male

76/2288

3.32 %

161/2422

6.65 %

<0.001

CRAI with sex worker

7/2436

0.29 %

12/2467

0.49 %

n.s.

CRAI with PWID

14/2437

0.57 %

40/2464

1.62 %

0.002

Gonorrhea b

141/2501

5.64 %

189/2501

7.56 %

0.004

Syphilis b

50/2501

2.00 %

47/2501

1.88 %

n.s.

Any STI b

317/2501

12.67 %

348/2501

13.91 %

n.s.

IDU with shared needles

9/2495

0.36 %

19/2501

0.76 %

n.s.

IDU

26/2501

1.04 %

28/2501

1.12 %

ns

Non-injection stimulant drug use (i.e. methamphetamine, cocaine, poppers, GHB, ketamine, XTC)

562/2501

22.47 %

759/2501

30.35 %

<0.001

CRAI, Condomless receptive anal intercourse; GHB, Gamma hydroxybutyrate; IQR, Interquartile range; IDU, Injection drug use; n.s., Not significant; PWID, Person who injects drugs; STI, Sexually transmitted infection; XTC, Ecstasy

a Median time between first and last test 714 days (IQR, 396–1191); individuals with <180 days between tests were excluded

b All self-reported diagnosis within the last 12 months

Risk behavior and number of tests in repeat testers

We also investigated risk increase between first and last test by number of tests by dividing the study cohort in three groups: those with 2 to 3 HIV tests (n = 1,384), those with 4 to 5 tests (n = 565), and those with 6 or more tests (n = 508). There was a positive association between number of tests and CRAI with an HIV-positive male, CRAI with a PWID, and non-injection stimulant drug use (Table 3).
Table 3

Risk behavior reported at the first and last test in individuals undergoing repeat HIV testing: individuals with <180 days between tests were excluded

  

2–3 HIV tests (n = 1384; 47 HIV+) median time first/last test 503 days (IQR, 317–845 days)

4–5 HIV tests (n = 565; 22 HIV+) median time first/last test 844 days (IQR, 567–1229 days)

6 or more tests (n = 508; 18 HIV+) median time first/last test 1333 days (IQR, 920–1705 days)

Risk factor/risk behavior (within 12 months prior to test)

Prevalence of AEH per individuals that reported the risk a

First Test

Last Test

 

First Test

Last Test

 

First Test

Last Test

 

Individuals reporting risk (n,%)

Individuals reporting risk (n, %)

Difference in percentage, relative (absolute)

P value b

Individuals reporting risk (n, %)

Individuals reporting risk (n, %)

Difference in percentage, relative (absolute)

P value b

Individuals reporting risk (n, %)

Individuals reporting risk (n, %)

Difference in percentage, relative (absolute)

P value b

10 or more male partners

3.8 %

456/1384 (32.95 %)

504/1384 (36.42 %)

+10.53 % (+3.47 %)

0.011

193/565 (34.16 %)

224/565 (39.65 %)

+16.07 % (+5.49 %)

0.012

198/508 (38.98 %)

223/508 (43.90 %)

+12.62 % (+4.92 %)

0.054

CRAI and 10 or more male partners

5.5 %

255/1384 (18.43 %)

301/1384 (21.75 %)

+18.01 % (+3.32 %)

0.006

101/565 (17.88 %)

132/565 (23.36 %)

+30.65 % (+5.48 %)

0.004

116/508 (22.83 %)

130/508 (25.59 %)

+12.09 % (+2.76 %)

n.s.

CRAI with HIV positive male

7.8 %

46/1277 (3.60 %)

72/1335 (5.39 %)

+49.72 % (+1.79 %)

n.s.

18/526 (3.42 %)

32/551 (5.81 %)

+69.88 % (+2.39 %)

n.s.

8/446 (1.79 %)

52/495 (10.51 %)

+487 % (+8.72 %)

<0.001

CRAI with sex worker

7.7 %

5/1350 (0.37 %)

6/1364 (0.44 %)

+10.45 % (+0.07 %)

n.s.

1/555 (0.18 %)

2/561 (0.36 %)

+100 % (+0.18 %)

n.s.

1/489 (0.20 %)

4/502 (0.80 %)

+300 % (+0.60 %)

n.s.

CRAI with PWID

10.3 %

9/1350 (0.67 %)

17/1365 (1.25 %)

+86.57 % (+0.58 %)

n.s.

5/555 (0.90 %)

13/559 (2.33 %)

+159 % (+1.43 %)

n.s.

0/491 (0 %)

9/498 (1.81 %)

+∞ (+1.81 %)

0.008

Any self-reported STI

4.6 %

155/1384 (11.20 %)

177/1384 (12.79 %)

+14.20 % (+1.59 %)

n.s.

64/565 (11.33 %)

87/565 (15.40 %)

+35.92 % (+4.07 %)

0.040

91/508 (17.91 %)

77/508 (15.16 %)

−15.35 % (−2.75 %)

n.s.

IDU with shared needles

7.8 %

4/1384 (0.29 %)

9/1384 (0.65 %)

+124 % (+0.36 %)

n.s.

2/565 (0.35 %)

5/555 (0.90 %)

+157 % (+0.55 %)

n.s.

0/508 (0 %)

3/508 (0.59 %)

+∞ (+0.59 %)

n.s.

Methamphetamine, not injected

6.2 %

77/1384 (5.56 %)

88/1384 (6.36 %)

+14.39 % (+0.80 %)

n.s.

28/565 (4.96 %)

35/565 (6.19 %)

+24.79 % (+1.23 %)

n.s.

26/508 (5.11 %)

38/508 (7.48 %)

+46.38 % (+2.37 %)

0.090

Non-injection stimulant drug use c

3.3 %

318/1384 (22.98 %)

411/1384 (29.70 %)

+29.24 % (+6.72 %)

<0.001

130/565 (23.01 %)

166/565 (29.38 %)

+27.58 % (+6.37 %)

0.002

96/508 (18.90 %)

163/508 (32.09 %)

+69.79 % (+13.19 %)

<0.001

AEH, Acute and early HIV infection; CRAI, Condomless receptive anal intercourse; IQR, Interquartile range; IDU, Injection drug use; n.s., Not significant; PWID, Person who injects drugs; STI, Sexually transmitted infection; a Among whole MSM study population after exclusion of those with newly diagnosed chronic HIV infection

b Calculated using McNemara test; P value displayed when below 0.1

c i.e. Methamphetamine, cocaine, poppers, Gamma hydroxybutyrate, ketamine, ecstasy

To investigate if there was an independent correlation between number of tests over time and change in risk behavior, we performed analyses using the Menza score at every single test as the outcome [23]. All tests with complete data on the variables of the Menza score (10,114 “Early tests” in 2,457 repeat testers) were included. In these 2,457 repeat testers, there was a significant increase of the score between baseline (median, 0; IQR, 0–3) and final test (median, 3; IQR, 0–5; P <0.001). The relationship between the number of tests and the logit of high scores (i.e. ≥5; at 28 % of visits subjects reported scores above this cutoff) demonstrates that there was a linear increase of the risk score with log number of tests up to the 17th test (Fig. 2). Univariable mixed-effects logistic regression models (Table 4) showed that more HIV tests (OR = 1.16 for each doubling of the number of tests, P <0.001), longer time since first HIV test (OR = 1.08 per year, P <0.001), and younger age (OR = 0.96 per 5 year increase of age, P = 0.048) were all individually associated with high risk scores (i.e. ≥5). In the multivariable mixed effects model, number of tests (OR = 1.18 for each log2 of the number of tests, P <0.001) and age (OR = 0.95 per 5-year increase, P = 0.006) had significant associations with high risk (i.e. ≥5 points), while time was no longer significant (time and number of tests were highly correlated (r = 0.67)).
Fig. 2

Percentage of high (i.e. ≥5 Menza-score points) HIV behavioral risk scores as modified from [23], by number of HIV tests in repeat testers. The bubble size reflects the denominator used to calculate the percentage of high scores. Linear Reg stands for linear regression, assuming a linear relationship between the y-axis (high risk score) and the x-axis (number of tests). Loess stands for local regression, a smoothing technique that allows one to see a pattern without assuming a particular distribution

Table 4

Mixed effects model on factors associated with high-risk behavior: (i.e. ≥5 Menza-score points)

Variables included in the mixed effects model

  

Univariable

Multivariable

 

N

Number of visits

OR (95 % CI)

P value

OR (95 % CI)

P value

Number of HIV tests (log2)

2457

10,114

1.16 (1.10–1.23)

<0.0001

1.18 (1.11–1.24)

<0.0001

Years since first test

2457

10,114

1.08 (1.04–1.13)

<0.0001

n.s.

Age (5-year increase)

2457

10,114

0.96 (0.93–1.00)

0.0479

0.95 (0.92–0.99)

0.0058

A significant association between the number of tests and high risk behavior was demonstrated also for risk behaviors not included in the Menza score, i.e. the combination of CRAI and 10 or more male partners, CRAI with a PWID, and non-injection stimulant drug use (Table 5).
Table 5

Associations of number of HIV tests with other risk factors: calculated by univariate mixed effects model by log2 increase of tests (i.e. between first, second, fourth, eight, etc.)

Risk factor

Number of individuals

Number of visits

OR for risk factor per log2 increase of number of tests

OR

(95 % CI)

P value

CRAI

2457

10,185

0.99

(0.94–1.04)

n.s.

CRAI and 10 or more male partners

2457

10,223

1.11

(1.05–1.18)

<0.001

CRAI with PWID

2455

10,098

1.35

(1.12–1.64)

0.002

CRAI with sex worker

2456

10,093

1.20

(0.88–1.64)

n.s.

Non-injection stimulant drug use (i.e. methamphetamine, cocaine, poppers, GHB, ketamine, XTC)

2457

10,251

1.37

(1.30–1.45)

<0.001

IDU

2457

10,251

0.91

(0.74–1.13)

n.s.

IDU with Shared needles

2455

10,209

0.95

(0.60–1.50)

n.s.

CRAI, Condomless receptive anal intercourse; GHB, Gamma hydroxybutyrate; IDU, Injection drug use; n.s., Not significant; OR, Odds ratio; PWID, Person who injects drugs; XTC, Ecstasy

Discussion

This is the largest modern study investigating the relationship between HIV risk behavior and HIV testing among MSM in the United States. Analysis of risk behavior in subjects participating in an AEH screening program (“Early Test”) over the past 6 years identified two major findings. First, MSM who screened repeatedly for HIV reported higher sexual risk behavior at baseline than MSM who only screened once. Second, repeat HIV testing was associated with an increase of risk behavior.

This is the first study to compare baseline risk behavior in single versus repeat testers in an era where serologic test results are available immediately and AEH can be diagnosed by NAT screening. Prior to the era of NAT screening, a handful of studies evaluated risk behaviors in repeat and single testers. In a study by Fernyak [8], the most frequent testers from 1995 to 1997 were those who practiced the highest risk behaviors, and demonstrated the highest incidence of HIV. In another study by MacKellar [24], repeat testers from 1994 to 1998 were more likely to report recent sexual risk behavior and acquire HIV when compared to first-time testers. We found that MSM in San Diego who repeatedly screened for HIV with combined serologic and NAT, practice higher sexual risk behavior at baseline than single testers, but there were no differences identified between these groups for most recreational drugs or recent STI.

This represents the biggest study to date analyzing the associations between repeat testing and risk behavior in repeat testers. In 2007, repeat testers accounted for more than 80 % of HIV tests performed annually in the United States [1], and when analyzing our program since 2008, repeat tests accounted for 48 % (8,396/17,333) of voluntary HIV tests among MSM. Our main finding was that repeat acute infection screening (i.e. rapid serologic testing plus NAT) was independently associated with an increase of risk behaviors between the baseline and the last test. Rates of 10 or more male partners, CRAI with an HIV-positive partner, CRAI with a PWID, non-injection stimulant drug use, and self-reported STIs all increased in line with testing frequency.

To assess change in risk over time we selected a previously validated score for prediction of HIV acquisition among MSM repeat testers as the outcome [23]. When evaluating that Menza risk behavior score at every single “Early test” visit, there was an increase of the proportion of individuals with high scores (i.e. ≥5 points) and the number of tests observed. In the study by Menza, this 5-point cut-off was associated with a cumulative 1-year HIV seroconversion incidence of 3.6 % (derivation cohort) and 4.4 % (validation cohort) [23]. This increase of risk behavior witnessed in our study is unlikely to simply reflect the nation-wide increases in risk behaviors in MSM over time [25], as number of tests – in contrast to time between first and last test – remained an independent predictor of increased risk-behavior in the mixed effects model. This finding may support a hypothesis that provision of negative AEH screening results, which provide HIV status information related to very recent risk behaviors, is independently associated with an increase of risk behaviors between the baseline and the last test. In other words, when a high-risk individual receives a negative HIV result, they may rationalize prior risk behavior as relatively safe, potentially justifying an escalation in subsequent risk [25]. However, although we describe a strong relationship between testing and risk behavior, we are not able to conclude causality, and also the opposite may be true (i.e. clients increased risk behavior is the cause and not the result of repeat testing). Nevertheless, our study provides provocative data that warrants further testing of causation in future studies.

Our study has several additional limitations including its single-center and retrospective design. Further, though pre-test counseling offering personalized risk reduction options was provided at every single “Early Test”, it is likely that the intensity of the counseling may have been reduced at some of the repeated visits. We hypothesized that the frequency of repeat testing is associated with increased risk behavior, but we cannot rule out that repeated tests are not a cause but just a result of increased risk behavior (i.e. our cohort increased their HIV testing as a response to their increased risk behavior). Additionally, testing in primary care settings and even home testing has become more normative during the study period and individuals may have tested outside the “Early Test” or associated studies, which may have led to an underestimation of the proportion of repeat testers. Further, some slight modifications of the previously validated Menza risk score were necessary to fit our available data and analyses. Finally, we cannot completely rule out that subjects reported risk behaviors more honestly with time and repeat testing.

Conclusions

In conclusion, this study demonstrates that the highest risk individuals for acquiring HIV (e.g. candidates for antiretroviral pre-exposure prophylaxis, referral for additional behavioral counseling, STI screening) that are usually identified by risk scores [26], may also be identified by their testing patterns. Strikingly, we found that repeat HIV testing itself might be associated with an increase in risk behavior. Although we were not able to conclude causality, this finding may support a hypothesis that rapid or immediate feedback of negative test results may reinforce high-risk behavior. To compensate for this reinforcement, intensified pre-test counseling among repeat testers may be necessary, particularly if increasing risk behavior is documented [27]. This might be true especially in the setting of acute infection screening, which provides HIV status information related to very recent risk behaviors. Future studies that assess different intensities and/or types of pre-test counseling (e.g. controlled trial randomizing intervention in high-risk repeat testers to reduce risk-taking behaviors) are needed to assess causality. Such information will be crucial to better understand the dynamics of risk-taking behavior and frame HIV test counseling messages. If true, then new prevention efforts should be developed and evaluated for MSM who repeatedly screen for HIV.

Notes

Abbreviations

AEH: 

Acute and early HIV infection

AUC: 

Area under the curve

CI: 

Confidence interval

CRAI: 

Condomless receptive anal intercourse

IQR: 

Inter-quartile range

MSM: 

Men who have sex with men

n.s.: 

not significant

NAT: 

Nucleic acid amplification testing

OR: 

Odds ratio

PWID: 

Person who injects drugs

ROC: 

Receiver operating characteristics

STI: 

Sexually transmitted infections

Declarations

Acknowledgements

This work was supported by funds from the Max Kade Foundation, New York (Max Kade Postdoctoral Research grant), the International Research Fellowship in NeuroAIDS (R25-MH081482), the Department of Veterans Affairs and grants from the National Institutes of Health (AI43638, AI074621, AI093163, AI100665, DA034978, AI036214, AI106039), CFAR, and the James B. Pendleton Charitable Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors wish to thank David A. Rodriguez, testing manager at the antiviral Research Center (AVRC), for his valuable advice and input.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
AntiViral Research Center, Division of Infectious Diseases, Department of Medicine, University of California, San Diego
(2)
Section of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Medical University of Graz
(3)
Division of Pulmonology, Department of Internal Medicine, Medical University of Graz
(4)
Veterans Affairs Healthcare System

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Copyright

© Hoenigl et al. 2015

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