Sexually Transmitted Diseases:
Measuring Sex Partner Concurrency: It’s What’s Missing That Counts
Nelson, Sara J. MPH*; Manhart, Lisa E. PhD*; Gorbach, Pamina M. MHS, DrPH‡; Martin, David H. MD§; Stoner, Bradley P. MD, PhD∥; Aral, Sevgi O. PhD¶; Holmes, King K. MD, PhD*†**
From the Departments of *Epidemiology, †Medicine, and **Global Health, University of Washington, Seattle, Washington; ‡Department of Epidemiology, University of California, Los Angeles, California; §Louisiana State University Health Sciences Center, New Orleans, Louisiana; ∥Departments of Anthropology and Medicine, Washington University, St. Louis, Missouri; and the ¶US Centers for Disease Control and Prevention
The authors thank all the Young Adults and Partnership Study participants as well as Anthony Archie, Masuma Bahora, Anne Buffardi, Ethel Green, Patrick Marshall, Anita Mehta, Tracy Peto, Lisa Ramachandra, and Khendi White for conducting the interviews. Additional thanks to Jennifer Wroblewski and Sabina Astete for performing the M. genitalium PCR assays, Fred Koch for compiling the Seattle STD clinic data, and Martina Morris for her helpful comments. Finally, the authors thank the clinicians and staff at the Public Health Seattle-King County, St. Louis County, and New Orleans Delgado Sexually Transmitted Diseases Clinics.
Supported by the University of Washington Sexually Transmitted Infections—Topical Microbicides Cooperative Research Center (STI-TM CRC) (NIH/NIAID A131448).
Correspondence: Lisa E. Manhart, PhD, UW Center for AIDS and STD, 325 9th Avenue, Box 359931, Seattle, WA 98104-2499. E-mail: firstname.lastname@example.org.
Portions of these data were presented at the 16th Biennial Meeting of the International Society for Sexually Transmitted Diseases Research (ISSTDR), July 10–13, 2005, Amsterdam, The Netherlands (MP-160).
Received for publication November 2, 2007, and accepted March 26, 2007.
Background: Sex partner concurrency is an important determinant of STI transmission dynamics, yet its measurement is not standardized.
Goal: We assessed the agreement, compared correlates, and investigated data quality and completeness between 2 common concurrency measures.
Study Design: Young adults (ages 18–26) attending public STD clinics between 2001 and 2004 in Seattle, St. Louis, and New Orleans, provided data on 2 or more sex partners in a computer-administered survey interview (N = 680). Concurrency with last partner was measured in 2 ways: (a) a direct question about other sexual contacts during the most recent sexual relationship and (b) overlapping start and end dates of the 2 most recent relationships.
Results: Although 56% reported concurrency by direct questioning and 54% by overlapping dates, the κ statistic for agreement between measures was only fair (0.395). Indeed, 29% of those reporting concurrent partners by the direct question did not do so by overlapping dates and 26% of participants concurrent by overlapping dates were not concurrent by the direct question. Each of the measures had dissimilar correlates, and concurrency data were missing or uninterpretable more often for the overlapping dates measure (21.3%) than the direct question (1.8%).
Conclusions: Concurrency was common by both measures but the measures were not interchangeable. Although the overlapping dates measure provided information about partnership duration, it is subject to missing or uninterpretable data. The direct question substantially minimized the amount of missing data and may be more appropriate for use with computer-administered survey interview.
SEX PARTNER CONCURRENCY, defined as having 2 or more sexual partnerships that overlap in time, amplifies the spread of sexually transmitted infections (STIs).1 Having concurrent partners removes the protective effect of partnership sequence,2 allowing pathogens to be acquired from 1 partner and transmitted to others during the overlapping period.3–5 Although having concurrent partners does not increase individual risk for acquiring STI beyond the risk associated with the number of partners, an individual’s concurrency may be a marker for participation in a higher risk sexual network and individuals with concurrent partners are often more likely to have STI.3,4,6,7
Concurrency has been assessed in a variety of ways. The most common methods include directly asking whether the individual had 2 or more sexual partnerships during a specified time period3,7,8; asking about start and end dates of sexual partnerships4,5,8–10; and having individuals keep coital diaries.11 Others have asked individuals if their partners have concurrent partners.5,12–14
Despite this range of concurrency measures, there have been few direct comparisons of the different methods. Overlapping dates of first and last sexual encounters between partners represents the most commonly used method and emerged from contact tracing data.4 Asking about individual partnerships, and implicitly constructing concurrency by calculating overlap, might facilitate more valid responses than asking explicitly about having concurrent partners15 and provides detail on the duration of overlap. However, this method requires accurate recall for dates of multiple partnerships, which may be challenging if partnerships are numerous or not particularly memorable. Furthermore, this approach may result in significant measurement error if the time between partnerships is short.16 In a previous random-digit dialing survey in Seattle, we found only fair agreement (κ = 0.34) when comparing concurrency assessed by a direct question versus overlapping dates.7 This suggests that the 2 measures may capture different phenomena, yet little is known about why they differ.
Given the increasing use of concurrency as a parameter in mathematical models of STI transmission, it is important to assess concurrency accurately. Understanding the differences between measures is an important step toward minimizing measurement error. Using data collected from young adult STD clinic attendees in 3 cities, we assessed agreement between the 2 most common measures of concurrency (direct question and overlapping dates), compared correlates, and explored reasons for differences between the measures.
Materials and Methods
Men and women attending public STD clinics in Seattle, New Orleans, and St. Louis, were recruited into the Young Adults, Partnerships, and STD Study between November 2001 and May 2004. Eligibility criteria included age 18 to 26 years and attending for a “new problem” (i.e., symptoms of, or suspected contact to someone with, STI). Potential participants in clinic waiting rooms were approached by a study interviewer, screened for eligibility, and provided a description of the study.
After informed consent, participants underwent an hour-long computer-assisted survey interview (CASI) consisting of questions from Wave III of the National Longitudinal Study of Adolescent Heath17 plus additional sexual behavior questions. Interviewer-administered questions elicited demographics, health characteristics, and detailed sociobehavioral information. A self-administered portion asked sensitive questions pertaining to sexual experiences, history of STI, delinquency, substance use, and detailed information about the 3 most recent romantic and sexual partners. The interview was followed by a routine clinical exam, unless patient flow patterns required that the exam occur first.
Clinical and Laboratory Methods
The clinical exam included elicitation of sexual history, symptoms, and inspection of the external genitalia. At each clinic, men provided a urine sample for Mycoplasma genitalium testing by PCR18 and a TMA assay.19 In Seattle, urine from men was also tested for Chlamydia trachomatis and Neisseria gonorrhoeae using the APTIMA CT/NG TMA assay (Gen-Probe, San Diego, CA). Among New Orleans and St. Louis men, a urethral swab was collected, and C. trachomatis and N. gonorrhoeae were detected using the Gen-Probe Pace2 and APTIMA CT/NG TMA assays, respectively. In both men and women genital herpes (HSV-2) infection was diagnosed by culture. All women received a speculum exam during which the clinician collected cervical swab specimens. C. trachomatis was detected by culture, and N. gonorrhoeae by Gram staining and culture in Seattle, by the Pace2 assay in New Orleans, and by the APTIMA CT/NG TMA assay in St. Louis. M. genitalium PCR18 and TMA assays19 were performed on a urine sample for all women and also on a cervical swab and a self-obtained vaginal swab for Seattle women. M. genitalium positivity was defined as a positive test result on any specimen by either test. Trichomonas vaginalis was diagnosed by the presence of motile trichomonads on wet-mount microscopy. All study procedures were approved by the institutional review boards at the University of Washington, Washington University, and Louisiana State University.
Measurement of Concurrency
In the self-administered portion of the questionnaire, participants listed up to 15 of their “romantic and sexual partners” in the previous 6 years, and ordered the 3 most recent sexual partners. To ensure that the partnership referenced in the direct question was theoretically the same partnership in the overlapping dates measure, analyses were restricted to participants listing 2 or more vaginal/anal sex partners. A small number who were not asked some partnership date questions because of a programming error were excluded.
Concurrency was defined as having another partner at any time during the relationship with the most recent partner and was measured by: (a) direct question, and (b) overlapping dates of the sexual relationships with the 2 most recent partners. In-depth questions asked about the dates of the first and most recent vaginal and/or anal sex with each partner (e.g., “In what month and year did you [first/most recently] have [vaginal/anal] intercourse with <Partner>?”). Concurrency measured by overlapping dates was computed by comparing the month and year of the first vaginal/anal sex encounter with the most recent partner to the last sexual encounter with the second partner. If the dates overlapped or were within the same month and year, the individual was classified as concurrent. No overlap was not concurrent. Following the date questions we asked: “Since you first had sex with <Partner>, how many other people did you have sex with during the sexual relationship?” Any response ≥1 indicated concurrent partners (i.e., concurrent), whereas a response of 0 indicated no concurrent partners (i.e., not concurrent).
Preprogrammed skip patterns required a response for each question, yet participants could opt out of a question by choosing “don’t know,” “decline to respond,” or “not applicable” as valid responses. Participants providing 1 of these responses for the direct question were classified as missing. Similarly, participants who opted out of reporting any year or month, which made it impossible to calculate overlap, were classified as missing for the overlapping dates measure. In addition, any out-of-sequence dates within a partnership (i.e., first sex happened before last sex) or between partnerships (i.e., last sex with the partner ordered “most recent” occurred before last sex with the partner ordered “second most recent”) were deemed uninterpretable and classified as missing for the overlapping dates measure.
Descriptive analyses of correlates of concurrency and missing data used Pearson χ2, Wilcoxon rank-sum, and Fisher exact tests. The κ statistic assessed the strength of agreement among concurrency measurements [0.00–0.20 (slight), 0.21–0.40 (fair), 0.41–0.60 (moderate), 0.61–0.80 (substantial), 0.81–1.00 (almost perfect)],20 and the McNemar χ2 tested whether the disagreement was random. Using multivariate logistic regression to identify independent correlates of concurrency, variables were selected for inclusion a priori if they were associated with concurrency in the literature or in univariate analyses (P < 0.10) (number of variables assessed = 38). Analyses involving missing data used only the Seattle and St. Louis data because the New Orleans questionnaire did not offer an opt-out option. STATA 9.0 was used for all statistical analyses (Stata Corp., College Station, TX).
Of the 1220 participants enrolled from Seattle (n = 605), St. Louis (n = 248), and New Orleans (n = 367), 158 (13.0%) did not answer any partner-specific questions, 203 (16.6%) answered questions about only 1 partner, 146 (12.0%) did not report vaginal/anal sex with both of their most recent partners, and 33 (2.7%) were omitted because of a programming error, leaving 680 (55.7%) in these analyses. Among these participants, 28.5% were diagnosed with an STI (M. genitalium, C. trachomatis, N. gonorrhoeae, T. vaginalis, or active HSV-2 lesions). M. genitalium was detected in 11.7%, C. trachomatis in 10.2%, N. gonorrhoeae in 6.2%, and T. vaginalis in 2.7%. The combined prevalence of STI among excluded participants (n = 540) was 38.7% compared with 28.5% among those who were included (P < 0.01).
The median age was 22 years, and 7.1% identified as gay or bisexual. Over half of the sample (52.8%) self-identified as black, and 32.5% identified as white. Over 80% of participants had a high school diploma or equivalent, and 4.1% had been married. The majority (70.5%) had been with their most recent partner for at least 3 months, and 65.0% were in a current relationship. A similar proportion of participants reported condom use at last sex with their most recent partner (39.4%) as with their second partner (41.2%).
Agreement of Concurrency Measures
Overall, 55.5% (371 of 668) of participants reported another partner concurrently with their most recent partner when explicitly asked (direct question) (Table 1). Concurrency measured by overlapping dates between the 2 most recent partnerships (53.5%; 286 of 535) was not significantly different (P = 0.38). Despite the similar prevalences, the κ statistic was 0.395, suggesting only “fair” agreement between the 2 measures. Although 69.0% (365 of 529) of participants reported concurrent partners by at least 1 measure, 29.2% (85 of 291) of those reporting concurrent partners by the direct question did not do so by the overlapping dates measure, and 26.4% (74 of 280) of participants reporting concurrent partners by overlapping dates did not report this by direct questioning. The McNemar χ2 test was not significant (χ2 = 0.76, P > 0.05) suggesting that the 2 types of discrepant responses were equally likely. Participants who reported concurrency by both measures had longer duration of overlap (mean = 22 months) than those who only reported overlapping dates (mean = 11 months) (P < 0.01). In sensitivity analyses, we added or subtracted 1 month from the date of most recent sex with the second partner. (When the second partnership began and ended in the same month, we added or subtracted 1 month from both the start and end dates; persons with missing or illogical dates were excluded from sensitivity analyses.) We found that the prevalence of concurrency measured by overlapping dates increased after adding 1 month (62.1%) and decreased after subtracting 1 month (41.9%) (Table 2). However, the agreement between the 2 concurrency measures did not substantially change (κ = 0.354 and 0.413, respectively). The mean gap between nonoverlapping partnerships was 16 months and did not differ between respondents with and without discrepant concurrency reports.
There were no differences in agreement between the 2 measures by gender. Agreement was better among St. Louis participants (κ = 0.49), whereas weaker agreement was observed among younger (18–20 years) (κ = 0.25), gay (κ = 0.23), and Hispanic (κ = 0.20) participants, as well as those without a high school diploma (κ = 0.22), without a current partner (κ = 0.30), or with a history of injection drug use (κ = −0.13).
Correlates of Concurrency
In univariate analyses, a composite diagnosis of “any STI” (M. genitalium, C. trachomatis, N. gonorrhoeae, T. vaginalis, or active HSV-2 lesions) was not associated with either concurrency measure (Table 3), nor with the respondent’s perception of their most recent partner having concurrent partners (data not shown). Furthermore, no individual STI were associated with either measure.
Several factors were consistently more common among respondents reporting concurrent partners, regardless of the measure used. These included: less than a high school education, history of arrest, recent ecstasy use, history of injection drug use, higher lifetime number of sexual partners, younger sexual debut, ever having sex the same day as meeting a partner, receiving money for sex, and self-reported history of STI. Both measures of concurrency were also associated with pregnancy and longer duration (>3 months) of the most recent relationship.
In contrast, several characteristics in these univariate analyses were associated with concurrency measured by the direct question but not by the overlapping dates measure. Although concurrency measured by the direct question was more common among participants who were black (P ≤ 0.05) or who had black partners (P ≤ 0.05), were in a current relationship (P ≤ 0.01), had experienced childhood sexual abuse (P ≤ 0.05), or had ever had anal sex (P ≤ 0.01), none of these characteristics was significantly associated with concurrency measured by overlapping dates. Notably, gender, age, sexual orientation, and marital status did not differ by concurrency status by either definition.
Multivariate Analyses of Concurrency Measurements
In multivariate analyses adjusted for study site and lifetime number of sexual partners (Table 4), only duration of the relationship >3 months and pregnancy with the most recent partner were significantly associated with both measures of concurrency. Injection drug use was the strongest correlate of concurrency identified by direct questioning, but was not significantly associated with the overlapping dates measure. Also independently associated with concurrency measured by direct question were childhood sexual abuse, being black, ecstasy use, and having a current partner. In contrast, concurrency measured by overlapping dates was significantly associated with receiving money for sex, ever having sex the same day as meeting a partner, a black partner, and condom use at last sex with the second partner. Neither current STI nor history of STI was associated with concurrency, irrespective of the measure used.
Patterns of missing data differed substantially between the 2 measures. Only 12 (2.3%) of the 529 Seattle and St. Louis participants opted not to answer the direct question, whereas 121 (22.9%) did not provide adequate data to calculate concurrency by overlapping dates. Explicitly opting out of a date question was the most common reason for missing the overlapping dates measure (52 of 529; 9.8%), followed by out-of-sequence dates between partnerships (49 of 529; 9.3%) and out-of-sequence dates within a partnership (20 of 529; 3.8%).
There were no differences in prevalences of laboratory-diagnosed STI, gender, race/ethnicity, education, or marital status between those who did and did not provide adequate data, irrespective of concurrency measure. Those missing responses to the direct question, however, were more likely to have ever received money for sex (41.7%) compared to those answering the question (12.8%) (P = 0.01). In contrast, participants missing data for the overlapping dates measure were more likely to have injected drugs (6.6% vs. 2.2%, P ≤ 0.05), and less likely to have used a condom during last sex with the most recent partner (32.2% vs. 42.2%, P ≤ 0.05) or to be in a current relationship (50.4% vs. 67.4%, P ≤ 0.01) than those providing sufficient data. They also reported fewer sex partners in the past month (mean 1.2 vs. 1.4, P ≤ 0.05) and were less likely to have an age-discordant relationship (18.2% vs. 29.2%, P ≤ 0.05) than those with complete data.
More than half of these STD clinic attendees reported concurrent partners, whether measured by a direct question or by identifying overlapping start and end dates of sexual relationships. However, agreement between the 2 measures was only fair. Neither measure of concurrency was independently associated with laboratory-diagnosed STI, and multivariate models developed using traditional significance criteria (P < 0.05) contained fairly different risk factors for each measure of concurrency. Furthermore, the amount of missing and uninterpretable data for the overlapping dates measure was substantially more than for the direct question. This likely contributed to the observed differences in statistically significant risk factors for concurrency between definitions. In fact, many (but not all) risk factors associated with 1 measure but not the other had similar odds ratios, and their lack of statistical significance was possibly due to reduced statistical power. Nevertheless, in studies relying on traditional significance testing, each measure would be associated with different risk factors.
There are several possible explanations for the poor agreement between the 2 methods. If participants did not perceive and list short-term or casual sexual encounters as partnerships, they would not have been asked about start and end dates of sexual activity. Consequently, this concurrency would not be identified by the overlapping dates measure, but may have been captured by the direct question. If this were true, we would expect a higher prevalence of concurrency using the direct question, yet the difference we observed was small. Alternatively, if 1 partnership ended and another began within the same month without overlapping, it would have been misclassified as concurrent by our overlapping dates measure. The slightly higher proportion of discrepant reports among participants concurrent by overlapping dates only, compared to that among respondents classified as concurrent by direct questioning only (29% vs. 26%), suggests this may have occurred in some cases. Another possibility is that participants could not remember or provide accurate dates of first and most recent sexual activity. Our finding that participants classified as concurrent by both measures had significantly longer average partnership overlaps than those classified as concurrent by overlapping dates alone suggests that some shorter overlaps may reflect reporting error, rather than true overlap. In sensitivity analyses, however, adding and subtracting 1 month from the last sex date of the second partner did not substantially affect the agreement between the 2 measures, possibly because the average gap between nonoverlapping partnerships was large. Education and literacy may influence the concordance between measures, and the finding that participants without a high school education had poorer agreement between measures than those with more education (κ = 0.22 vs. 0.42) supports this. Neither measure may properly identify concurrency if relationships with 1 person started and ended multiple times, or if the respondent continued to have sex with this partner after the “relationship” ended. Finally, the overlapping dates measure only considered vaginal and/or anal sex partners whereas partners referenced in the direct question could have included oral sex partners.
Problems with missing and/or uninterpretable data likely contribute to the discrepancies. Previously, using telephone interviewers, we found that 10% of participants did not answer the direct question and 19% did not provide sufficient data to calculate overlapping dates.7 Using CASI and preprogrammed skip patterns, the current study found lower rates of missing data for the direct question, but a higher proportion of missing or uninterpretable data for the overlapping dates measure. Ten percent of our study population was overtly missing the necessary data and 13% reported out-of-sequence dates either within or between partnerships. A human interviewer may have been able to reconcile these date errors and provide other memory cues to help respondents recall forgotten partners.21 Alternatively, a more sophisticated CASI instrument could be programmed to reconcile out-of-sequence dates. The dates or the partnerships could be resequenced to correct this problem, but given the uncertainty about which was correct (partnership or date order), we excluded such people.
This study expands on earlier work investigating differences between 2 common measures of concurrency. The direct question was simple and yielded relatively complete data, and the self-administered CASI methodology probably increased willingness to answer the question.22 However, it may be less appropriate in face-to-face interviewing. Inquiring about the first and last dates of recent sexual partnerships allows respondents to indirectly report concurrency, provides information on the duration of overlap, and introduces only minimal bias by inaccurate recall of dates if the time between partnerships is large.16,23 However, this method requires adequate responses to multiple questions. Incomplete data for any of these renders the overlapping dates measure missing. Although coital diaries can elicit more complete, prospective data on sexual behavior and concurrency, they are not feasible for cross-sectional and/or case–control study designs. A partner's concurrency increases risk for STI, independent of one's own concurrency or number of partners,1 but knowledge of partner behavior is often poor.12,13,24
Restricting our analyses to participants who answered questions about their 2 most recent sexual partners ensured that each respondent had the opportunity to report a concurrent partner by either measure, but excluded those with only 1 partner (not concurrent by definition) from prevalence estimates. Furthermore, excluded participants who did not answer partner-specific questions were more likely to have STI, suggesting that some of the highest-risk individuals were not included in our analyses. However, after adjusting for study site the association between STI and this excluded subset was no longer significant; thus, any bias is likely small. We did not consider partnership type, and overlap between a main and casual partner may be a more accurate measure among adolescents.6 Finally, neither of these measures captures all aspects of concurrency germane to population-level STI transmission dynamics. From a transmission standpoint, only concurrency occurring during the infectious period of a given STI is relevant, and these periods vary for different pathogens.25,26 Thus, the frequency and duration of sexual activity with each concurrent partner is important, but only the overlapping dates measure captures duration and neither measure captures frequency. Complete network data with better information on the natural courses of infectivity for specific STI is needed to more accurately determine the impact of concurrency on transmission dynamics.
Concurrency is an important parameter in mathematical modeling and should be measured consistently. Our findings demonstrate that the most common measures of concurrency are not interchangeable and, among these young adult STD clinic attendees using CASI, the direct question provided more complete data. Incorporating a human interviewer, more sophisticated CASI programming, an event-history calendar, or other memory aids may result in more accurate reporting for the overlapping dates measure, but further studies are required to empirically demonstrate this.
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