1 National Center for Chronic Disease Prevention and Health Promotion, Division of Reproductive Health, Centers for Disease Control and Prevention, Atlanta, GA
2 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
3 Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
4 Jefferson County Department of Health, Birmingham, AL
5 National Center for HIV, STD, and TB Prevention, Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA
6 Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
7 Department of Microbiology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
Correspondence to Dr. Lee Warner, Women's Health and Fertility Branch, Division of Reproductive Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, 4770 Buford Highway NE (Mailstop K-34), Atlanta, GA 30341 (e-mail: dlw7{at}cdc.gov).
Received for publication July 12, 2004. Accepted for publication November 12, 2004.
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ABSTRACT |
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chlamydia; confounding factors (epidemiology); contraceptive devices, male; cross-over studies; epidemiologic methods; gonorrhea; HIV infections; sexually transmitted diseases
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INTRODUCTION |
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Inconsistent evidence for condom effectiveness is partly attributable to limitations in study design (5, 7
, 11
21
), including the potential for uncontrolled confounding. Unmeasured differences between condom users and nonusers can confound estimates of condom effectiveness, particularly in studies of curable STIs (e.g., gonorrhea and chlamydia) (19
23
). For example, recent data (14
, 21
24
) indicate that condoms are used more often with partners who are likely to be infected (e.g., occasional, new, or "one-time" partners) than with partners who are unlikely to be infected. Since the infection status of partners is unknown in studies of curable STIs, this usage pattern could introduce confounding and lead to underestimation of condom effectiveness (21
).
We hypothesized that the case-crossover method (25) would reduce confounding of associations between condom use and curable STI in studies where condom users differed from nonusers in terms of important unmeasured characteristics. In the case-crossover design, patterns of condom use during intervals in which infection was diagnosed in a single individual would be compared with patterns of use for that same individual during intervals in which infection was not diagnosed. Using study subjects as their own controls can reduce confounding by subject characteristics that do not change over time, including characteristics that are difficult or impossible to measure. Contrasting intervals within the same individual with the case-crossover design could improve comparability between intervals in which condoms were used and intervals in which they were not, even for unmeasured characteristics such as exposure to an infected partner. Thus, we evaluated condom effectiveness for prevention of gonorrhea and chlamydia by applying the case-crossover design to a large study of barrier contraception in women (13
), where corresponding cohort analyses (26
) suggested a weak protective effect for condoms.
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MATERIALS AND METHODS |
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At the initial visit and at each scheduled follow-up visit, participants received diagnostic examinations for gonorrhea, chlamydia, trichomoniasis, bacterial vaginosis, syphilis, and herpes simplex viruses 1 and 2. Gonorrhea and chlamydia were defined as a cervical culture that was positive for Neisseria gonorrhoeae or Chlamydia trachomatis. At the initial visit, participants also completed a behavioral interview and were taught how to record information on sexual activity and the use of barrier contraceptives in a prospectively maintained diary. At each follow-up visit, participants reviewed diary information with project staff, completed a brief interview on sexual behavior during the interval, and received a 6-week supply of the chosen barrier contraceptive. Project staff counted all products returned by participants, including unused condoms and condom wrappers, to validate self-reported information on condom use. For interim (unscheduled) visits, participants also received diagnostic examinations and were given appropriate treatment if found to be infected. When an interim visit occurred, diary information was used to divide data on condom use and sexual activity into two intervals (i.e., the interval between the last scheduled visit and the interim visit and the interval between the interim visit and the next scheduled visit). Participants diagnosed with gonorrhea or chlamydia (or another curable STI) at study entry or during follow-up were treated according to the clinical guidelines issued by the Centers for Disease Control and Prevention; thus, participants were presumed to be disease-free both at study entry and at the beginning of each interval. All study procedures and forms were reviewed and approved annually by the University of Alabama at Birmingham Institutional Review Board.
Statistical analysis
We compared estimates of the effectiveness of condoms against gonorrhea and chlamydia obtained from the case-crossover analysis with estimates obtained from the cohort analysis. Both analyses were conducted using the time interval between scheduled monthly visits (i.e., between the initial visit and the first follow-up visit or between two follow-up visits) as the unit of analysis. Thus, each interval ended either with a diagnosis of incident gonorrhea/chlamydia or without such a diagnosis. The case-crossover analysis included all intervals in which sexual activity was reported among participants who experienced a "crossover" in infection status during follow-up. Thus, included participants had both intervals that ended in a diagnosis of incident gonorrhea or chlamydia (case intervals) and intervals with no such diagnosis (noncase intervals). (Participants who had only case intervals or only noncase intervals were excluded from case-crossover analysis.) Because participants were at risk for recurrent infection throughout follow-up, we allowed participants to have multiple case intervals and noncase intervals, regardless of whether the latter occurred before or after the first incident STI diagnosis.
The prospective cohort analysis included all time intervals in which sexual activity was reported among all participants. Thus, the key difference between the two analytical approaches is that the cohort analysis also included intervals from participants who were diagnosed with gonorrhea or chlamydia at each follow-up visit and intervals from participants who were not diagnosed with gonorrhea or chlamydia at any follow-up visit. As with the case-crossover analysis, we allowed participants to have multiple case and noncase intervals in the cohort analysis.
Measures
For both analyses, we assessed the association between condom use and incident gonorrheal or chlamydial infection during each time interval. A combined outcome was used to increase precision after initial analyses for each STI produced similar results. Condom use during each interval was categorized as follows: 1) consistent use (100 percent use) with neither breakage nor slippage; 2) consistent use with either breakage or slippage; 3) inconsistent use (199 percent use); or 4) nonuse. The percentage of condom use during the interval was calculated by dividing the number of acts of vaginal intercourse in which condoms were used by the total number of acts of vaginal intercourse. In each analysis, we also assessed associations between the number of unprotected sex acts reported during the interval (i.e., 0, 110, or >10 unprotected acts) and incident infection. In contrast to measures of the percentage of condom use, measures of the number of unprotected sex acts may permit more accurate evaluation of the dose-response relation between exposure and infection (12, 19
, 21
).
Analytical procedures
Case-crossover analysis.
For the case-crossover analysis, we used conditional logistic regression to evaluate the association between consistent condom use (or number of unprotected sex acts) and incident infection. In the case-crossover analysis, the participant was used as the matching factor, and the set of follow-up intervals from that participant constituted the observations within each matched stratum. The model included: 1) consistency of condom use during the interval, as the primary exposure, and 2) time-dependent factors during the interval that were known risk factors for these STIs, as potential confounders. Time-dependent risk factors included: number of sex partners (>1 vs. 1); type of sex partner (new vs. not new); use of vaginal spermicide (yes vs. no); and total number of sex acts, in models assessing the consistency of condom use as the primary exposure, or number of protected sex acts, in models assessing the number of unprotected sex acts as the primary exposure. Time-independent factors (e.g., race/ethnicity, age <25 years) were not included as potential confounders, since these factors are automatically controlled by the case-crossover design (27). Time-independent factors (along with time-dependent factors) were evaluated as potential effect modifiers of the association between condom use and infection; however, no clear patterns of interaction between these factors and condom use emerged, and these product terms were subsequently removed from the model.
A case-crossover analysis, by design, includes multiple responses from the same subject. However, these responses are conditioned by subject in the conditional logistic regression model through the inclusion of a subject-specific fixed effect and are assumed to be independent (27). Thus, no additional measures were used to adjust for correlation of responses in this analysis.
Cohort analysis.
For the cohort analysis, we used unconditional logistic regression to evaluate the association between consistent condom use (or number of unprotected sex acts) and incident infection. The general modeling strategy used in the case-crossover analysis was also used in the cohort analysis. Condom use was defined as described above, and the same time-dependent factors were included. Additionally, time-independent (i.e., fixed) factors were included in this model, including age (<25 years vs. 25 years), race/ethnicity (Black vs. other), education (more than high school vs. high school or less), marital status (married/cohabiting vs. not married/cohabiting), and recent history of gonorrhea or chlamydia (yes vs. no).
Because the cohort analysis included multiple responses from the same subject but the unconditional logistic regression model contained no subject-specific fixed effect, we adjusted these models for the correlation of standard errors between responses, using the generalized estimating equations method (28, 29
). Different correlation structures assessed provided similar results, which suggested that there was weak correlation among the set of responses contributed by individual participants (i.e., all correlation coefficients were less than 0.05). The exchangeable correlation structure was used, assuming that the correlation of responses across intervals remained the same for a given participant.
We used the risk odds ratio (ROR) (with 95 percent confidence intervals) as the effect measure for both the case-crossover and the cohort analyses to ensure that any difference in estimates would be due to the analytical approach, rather than the choice of effect measure. We estimated the ROR to contrast the odds of infection during intervals in which condoms were used with those of intervals in which they were not. Likewise, we used the ROR to assess the relative odds of infection during intervals in which unprotected sex acts were reported as compared with intervals in which they were not. For the case-crossover analysis, we directly estimated the logarithm of the ROR by means of the conditional logistic regression coefficient. For the cohort analysis, we arrived at the corresponding ROR estimate by contrasting case intervals with noncase intervals only. This is a slight departure from contrasting case intervals with all intervals to estimate the risk ratio. Since case intervals accounted for only 6 percent of follow-up intervals in our analysis, our ROR estimates closely approximate risk ratios. We made this slight approximation error in the cohort analysis to ensure that the measure of association paralleled that used in the case-crossover analysis.
All statistical analyses were conducted using SAS, version 8.2 (SAS Institute, Inc., Cary, North Carolina). Results were considered statistically significant at p 0.05, and all statistical tests were two-tailed.
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RESULTS |
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Estimates of the strength of association between consistent condom use and incident gonorrhea and chlamydia varied markedly between the case-crossover and cohort analyses (table 2). In the case-crossover analysis, participants were diagnosed with infection approximately half as frequently during intervals in which they reported consistently using condoms as during intervals in which they reported not using condoms at all (adjusted ROR = 0.52, p = 0.04) (data not shown). Protection from consistent condom use relative to nonuse was stronger during intervals in which participants did not report breakage or slippage (adjusted ROR = 0.49, p = 0.03) than during intervals in which they did (adjusted ROR = 0.72, p > 0.20). Inconsistent condom use was also associated with reduced odds of infection in comparison with nonuse (adjusted ROR = 0.64), but this finding was not statistically significant (p = 0.12). Among other time-dependent predictors, only reporting multiple partners during the interval was significantly associated with increased odds of infection (adjusted ROR = 1.8).
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Associations between the number of unprotected sex acts reported and incident infection also differed between the case-crossover and cohort analyses (table 3). In the case-crossover analysis, a significant dose-response relation existed between the number of unprotected sex acts and infection (2 test for linear trend:
2 = 6.90, p = 0.009). Compared with intervals in which no unprotected acts were reported, the adjusted odds ratios for infection were 1.4 for intervals with 110 unprotected acts and 2.6 for intervals with more than 10 unprotected acts. In the cohort analysis, this trend was attenuated markedly and no longer statistically significant (
2 test for linear trend:
2 = 1.96, p = 0.18). Consistent with findings from a previous analysis (21
), no significant linear trend was found between the number of acts that were protected by condoms and infection in either the case-crossover analysis (
2 test for linear trend:
2 = 0.07, p = 0.80) or the cohort analysis (
2 test for linear trend:
2 = 0.02, p = 0.89) (data not shown).
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DISCUSSION |
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Studies of condom effectiveness in which users differ from nonusers with regard to factors that are not easily measured may provide biased findings because of uncontrolled confounding. We attribute the difference in condom effectiveness estimates between our two analyses to the increased control of unmeasured confounding provided by matching (27) with the case-crossover design. By matching individuals with themselves, the case-crossover design effectively reduced confounding from subject characteristics that remained constant across intervals, regardless of whether they were measured. (Time-independent characteristics (e.g., race/ethnicity) do not vary within an individual in the case-crossover design.) Although we, along with other investigators (13
, 19
, 21
, 22
), have documented unmeasured differences in exposure to infected partners as a likely source of confounding in condom effectiveness studies, we cannot exclude the possibility that the reduction in confounding observed in the case-crossover analysis was attributable to control of other unmeasured factors (e.g., the circumcision status of male partners, the presence of cervical ectopy). Identifying the specific confounding factor(s) that were reduced is less important, however, provided that the overall comparability between the exposed and unexposed groups (i.e., intervals with and without condom use) is increased (30
).
Our results are consistent with those reported in a cross-sectional analysis (21) of data from Project RESPECT (31
), which also used a design technique (i.e., restriction) to reduce confounding from unmeasured differences between users and nonusers in estimates of condom effectiveness for preventing gonorrhea and chlamydia. In contrast to this analysis, those investigators (21
) explicitly distinguished study participants with known exposure to infected partners, who visited the clinic because they were notified of this exposure by their partner or the health department, from participants for whom partner infection status was unknown. Similar to our findings presented here, there was a stronger association between consistent condom use and STI prevention when analyses were restricted to participants with known exposure to infection than for participants for whom exposure was unknown. STI risk also increased with the number of unprotected sex acts only among participants known to have infected partners. That analysis, like ours, suggests that the expected benefits of condom use are most likely to be observed when comparisons of condom use and nonuse are similar with regard to potentially confounding characteristics.
Our application of the case-crossover design had some limitations. First, although the case-crossover design eliminates opportunities for many types of confounding, it does not eliminate confounding by factors that change over time within individuals but are not easily measured. For example, because exposure to infected partners could not be directly measured, we could not exclude the possibility that, even within a participant, exposure to infected partners occurred more often during intervals when condoms were used than during intervals when they were not. Women selected for the case-crossover analysis may have been more likely to use condoms during intervals when they were at higher risk of infection but not during intervals when risk was lower. Previous reports from this clinic population support this hypothesis: Condom use was approximately 60 percent higher when participants reported having new or casual partners than when they had regular partners (24). Thus, although the case-crossover design probably reduced confounding from unmeasured differences between users and nonusers, this example demonstrates that our study still suffered from residual within-person confounding. Confounding from differential exposure to infected partners has previously been shown to contribute to underestimation of condom effectiveness (21
).
A second limitation is that both the case-crossover and cohort analyses may have been subject to other biases, including misclassification of condom use and infection status. Of concern is our finding that approximately one third of gonoccocal or chlamydial infections were diagnosed during intervals in which participants reported using condoms consistently without breakage or slippage. These infections could have resulted from actual condom failure or from other factors, including overreporting of condom use (18, 32
), imperfect diagnostic test performance (33
), or incomplete measurement of condom use problems (e.g., delayed application of condoms) (34
, 35
). Although such factors generally contribute to underestimation of condom effectiveness (17
, 21
, 23
, 36
), we could neither discern the exact cause of these infections nor quantify the amount of residual bias that may have been present. Our study also may have underestimated the incidence of chlamydia because of the reduced sensitivity of culture for this infection as compared with gonorrhea. However, cultures for chlamydia (and gonorrhea) were performed at each follow-up visit, independent of collection of data on clinical or behavioral variables (including condom use). Given that these and other potential biases should have been present in both sets of analyses, these factors are unlikely explanations for the difference in effect measures observed between the case-crossover and cohort analyses. Thus, while the case-crossover analysis provided increased control of confounding as compared with the cohort analysis, our findings represent only a minimum estimate of the effectiveness of condoms against these infections.
These limitations are offset by the strengths of our approach. First, the cohort study we examined had many desirable features for assessing and comparing condom effectiveness between two different analytical approaches. Specifically, the study included prospective collection of data on condom use through a quality-controlled daily diary; detection of incident (versus prevalent) infection; ascertainment of participants' condom use before STI test results were known; and use of multiple follow-up visits (allowing for the conduct of case-crossover analyses). Second, with the case-crossover design, we identified an analytical strategy with which to circumvent unmeasured confounding and reduce its impact on estimates of condom effectiveness in preventing gonorrhea and chlamydia. To our knowledge, this is the first application of the case-crossover method to studies assessing the association between condom use and STI. Although the case-crossover approach (25) has been applied to a variety of other situations (37
) (including triggers of myocardial infarction (25
, 38
41
), the relation between air pollution and mortality (42
44
), and risk factors for motor vehicle accidents (45
, 46
) and childhood injuries (47
, 48
)), we are not aware of other published studies that have used this design to examine condom effectiveness. Moreover, since case-crossover analyses are frequently undertaken because an adequate comparison group is lacking, our work represents one of the few published studies (37
, 49
) to have directly compared results from case-crossover analyses with those from more conventional analyses.
The major strength of our approach was our ability to compare two different analytical approaches within the same study and to empirically demonstrate a difference in the effect measure for condom use observed using an analytical strategy specifically designed to reducing confounding. Increased control of unmeasured confounding is the most likely explanation for the stronger protective effect of consistent condom use and the stronger dose-response relation between the number of unprotected sex acts and infection observed in the case-crossover analysis. All participants in both analyses were recruited with the same criteria, were assessed with identical instruments during the same time period, and received the same diagnostic evaluations. Both analyses presented also included approximately the same set of case intervals; further analyses that restricted the case-crossover and cohort analyses to exactly the same set of case intervals did not alter our results (data not shown). These factors indicate that differences in the analytical strategy used are responsible for the difference in results observed between the two analyses. This finding holds important implications for properly interpreting the inconsistent results observed in other studies of condom effectiveness for prevention of gonorrhea and chlamydia. Our results suggest that epidemiologic studies of condom effectiveness are probably confounded by unmeasured differences between condom users and nonusers, and that the likely result of such confounding is underestimation of the effectiveness of condoms against these infections.
In summary, our application of the case-crossover design to condom effectiveness studies improved the validity of estimation in a context in which unmeasured confounding was suspected and there was at least one known explanation for the confounding. Given the difference in condom effectiveness estimates between our case-crossover and cohort analyses, we encourage other investigators to explore this analytical strategy. Applications of the case-crossover design appear to hold promise for research on STIs and human immunodeficiency virus and other areas of epidemiologic investigation in which unmeasured confounding presents a direct threat to the validity of study findings.
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ACKNOWLEDGMENTS |
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The authors thank Ward Cates, Michael Cannon, and Thomas Peterman for their helpful suggestions on the manuscript.
The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does the mention of trade names, commercial products, or organizations imply endorsement by the US government.
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References |
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