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Sexually Transmitted Diseases:
Original Article

Sexual Mixing Patterns of Patients Attending Sexually Transmitted Diseases Clinics

GARNETT, GEOFFREY P. PhD*; HUGHES, JAMES P. PhD; ANDERSON, ROY M. PhD, FRS*; STONER, BRADLEY P. MD, PhD; ARAL, SEVGI O. PhD†; WHITTINGTON, WILLIAM L. PhD†; HANDSFIELD, H. HUNTER MD†§; HOLMES, KING K. MD, PhD

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Author Information

*From the Centre for the Epidemiology of Infectious Disease, Department of Zoology, Oxford University, United Kingdom; the Center for AIDS and STD and the §Seattle‐King County Department of Public Health, University of Washington, Seattle; and the Centers for Disease Control, Division of STD and HIV Prevention, Atlanta, Georgia

Reprint requests: Geoffrey P. Garnett, PhD, Department of Zoology, University of Oxford, Wellcome Centre for the Epidemiology of Infectious Disease, South Parks Road, Oxford OX1 3PS, England.

Received for publication April 25, 1995, revised September 12, 1995, and accepted September 15, 1995.

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Abstract

Background:: Theoretical studies have highlighted the importance of patterns of choice of sex partner in the transmission and persistence of sexually transmitted diseases (STDs).

Goal:: To describe reported patterns of sexual mixing according to numbers of sex partners in STD clinics.

Study Design:: Patients attending public health clinics in Seattle, Washington were interviewed about their own and their partners' behaviors.

Results:: Throughout, patterns of sexual mixing were weakly assortative. Across activity groups, many respondents believed their partners had no other sexual contacts. Those with three or more partners frequently perceived their partners to have three or more partners as well.

Conclusions:: Assortatively mixing persons of high sexual activity makes the persistence of STDs within a population likely (i.e., they act as a “core group”). Additionally, because mixing is not highly assortative (like with like), a steady trickle of infection from members of the core group will pass to other segments of the population.

THE AIDS PANDEMIC has stimulated much research on the prevailing patterns of human sexual behavior and how these vary in different risk groups, communities, and countries. Research is diverse with respect to aims, methods and the degree of quantification. The origin of quantitative studies of sexual behavior is widely accredited to Kinsey and Pomeroy,1,2 who examined sexual experiences in men and women in the United States during the 1930s and 1940s. Methodologic problems of these early surveys have been discussed widely.3 A key problem is the nature of the sample; Kinsey and coworkers used a convenience sample of volunteers, a high fraction of whom were students at the time of interview.4

In the AIDS era, a series of detailed surveys has been conducted in which great effort has been expended to ensure the participation of persons chosen at random from a defined population, with careful note taken of stratification of the sample by social, economic, and demographic variables. Of the two largest, one was conducted in the United Kingdom (sample size, 18,876 persons) and was based on face‐to‐face interviews, and one was conducted in France (sample size, 20,055 persons) by telephone.5,6 Participation rates in these studies (defined as the fraction of those contacted who agreed to participate) were 71.5% (UK) and 76.5% (France). Recently, another large study, the National Health and Social Life Survey (NHSLS), was published. This study recorded face‐to‐face interviews with a random sample of 3,155 males and females resident in the United States, and its response rate was 80%.7

These three surveys reveal much detail about quantitative characteristics of sexual behavior, such as the frequency distributions of both the number of different sex partners in defined time periods (e.g., the past year) and the number of sex acts of a defined type per partner or per interval of time, stratified by various socioeconomic and demographic variables. Studies8–10 of the transmission dynamics of sexually transmitted infections, such as gonorrhea and human immunodeficiency virus (HIV), highlight the importance of these distributions as determinants of observed epidemiologic pattern. Survey studies reveal many similarities in the form of these distributions in each of the three countries as well as some interesting differences (e.g., in the distribution of reported sex acts per unit of time).

An important role for these national surveys is as templates against which other studies, in particular those concerning risk groups or smaller communities, can be assessed. For example, much epidemiologic research on sexually transmitted diseases (STDs) is based on patients attending STD clinics. Behavioral data often are collected from these patients to assess the importance of different behavioral attributes as risk factors for infection. Information on patients attending the clinics can be “mapped” onto the population‐based sample distribution from the national surveys, to see whether the average or variability of a particular variable measured in the clinic differs from those recorded in the general population. One example is the number of different sex partners recorded, for instance, in the past year. Data from STD clinic‐based surveys suggested that patients, on average, report many more partners than is typical in the general population. Those with many partners form a core group of highly sexually active individuals who play a major role in ensuring the persistence of many, if not all, STDs within the larger community.8–10

Aside from numbers of sex partners, other variables play an important role in the epidemiology of STDs. One in particular is of major importance, namely, the pattern of mixing within and between different sexually active groups within the community.11–13 These groups may be characterized by age, residence location, ethnicity, socioeconomic factors, or behavioral variables, such as the rate of sex partner acquisition. In practical terms, it is difficult to study sex networks because people must divulge the names and addresses of their sex partners so that they can be interviewed in an expanding survey of network structure, to assess similarity (or concordance) in behavioral, social, and demographic characteristics. Few persons are willing to do so unless the context offers some benefit either to the person interviewed or to their sex partners. One setting in which such a benefit results is an STD clinic, in which contact tracing is used to facilitate the treatment and/or counseling of an infected patient's recent sex partners. The contacted partners can then be interviewed with the aim of identifying the next set of sexual contacts.

To date, quantitative studies of network structure are few. Most of the published information concerns mixing by age group in heterosexual communities because the necessary data can be acquired by survey (i.e., the identity of a partner is not required if his or her age is known).14,15 One study16 attempted to identify network structure by snowball interviewing within a cooperative gay male community in Iceland. In two other studies,17,18 contact tracing within an STD clinic was used to construct networks on the basis of residence location (i.e., a spatial network) within large urban settings. Garnett and Anderson19 have used the spatial contact data in these two studies to estimate the degree of assortative (partners more likely to be chosen from the same residence area) or disassortative (partners more likely to come from other areas) mixing. With similar methods, Anderson et al20 used age‐stratified data to construct mixing matrices between males and females in different age classes. Studies by Granath et al21 in Sweden and Hsu Schmitz and Castillo‐Chavez22 in the United States have begun to address the estimation of mixing matrices between groups with different levels of sexual activity (defined on the basis of sex partner numbers per unit of time) in heterosexual communities.

In this article, we report a detailed study of sexual mixing patterns between different sexual activity classes, as defined by partner numbers, based on contact tracing studies in two health clinics in Seattle in the United States. The aim is to assess the degree of assortative mixing within the adult patient populations, based on sexual behavioral data stratified by ethnicity and by social and demographic characteristics. Sexual behavior characteristics of the study population are examined in the context of the data from the NHSLS.7

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Methods

Study Population

Patterns of sex partner choice are estimated from interviews with patients attending two health clinics in Seattle, Washington. The clinics are the Seattle King County Department of Public Health STD Clinic at Harborview Medical Center and an STD service at Columbia Health Center (also run by the Department of Public Health). This study was reviewed and approved by the University of Washington Human Subjects Review Committee.

At the Harborview Medical Center, patients with STD are seen primarily on a walk‐in basis for STD screening and for evaluation of specific STD‐related symptoms. Patients attending the STD clinic suspected they were infected with an STD, and they clearly represent a group of the population at high risk for STD infection. From 1992 to mid‐1993, patients were recruited for enrollment in the study from among all heterosexuals between the ages of 14 to 45 years of age seeking care during a randomly assigned 90‐minute segment of each day. All patient charts were reviewed, and the first patient meeting enrollment criteria was invited to participate. If this person refused, the next eligible person was approached. The refusal rate was 37%. From June 1993, enrollment criteria were modified to limit enrollment to patients with symptoms or with a history of sexual exposure to a partner with STD. Patients from both groups make up sample A described in this article. Another sample was recruited from those patients in whom it was deemed likely that follow up would be successful (i.e., each patient had a permanent address and telephone number, was a resident of King County, had no history of intravenous drug abuse, and had not participated in any other research study). This group was treated separately as sample B. Another sample came from the Columbia Health Center, a pediatrics clinic that serves the predominantly minority populations of southeast Seattle. A high proportion of patients are young women seeking family planning services, reproductive health care, or STD care. Recruited patients were those suspected to have STD infection and referred to the STD clinician (making this population highly comparable to the STD clinic group). Enrollment criteria were the same as those used at the Harborview STD clinic. At both sites, exclusion criteria were age younger than 14 or older than 45 years, primarily homosexual orientation, and inability to understand spoken English. In addition, pregnant women and persons with major health disorders were excluded.

Data on a wide range of issues, including demographic and behavioral characteristics, were collected using face‐to‐face interviews. Information was sought on both the sexual behavior of patients and on the characteristics of their sex partners. A total of 973 different sex partnerships were identified from 619 patients. Subjects were interviewed on subsequent occasions at 3‐month intervals and often reported additional partners. However, all the data reported here were acquired on the first visit. The demographic information on the patients from each clinic is summarized in Table 1. As expected, there are fewer men than women in the sample from the Columbia Health Center, and these patients are significantly younger than those from the STD clinic. For the Harborview STD clinic samples, the ratio of men to women was 1.2 to 1. The number of partnerships identified by men and women from which mixing matrices could be estimated also are presented.

Table 1
Table 1
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Study Design and Analysis

Patients were asked how many sex partners they had in the preceding 3 months and how many sex partners (including themselves) they perceived their partners to have had in that period. From this information, it was possible to derive mixing matrices based on the number of sex partners over 3 months. The period of 3 months is short enough to be relevant for a study of current sexually transmitted diseases and to allow for relatively easy recall. Patients were asked about the number of other sex partners they thought their partners had in the past 3 months and over their lifetimes. In addition, they were asked about their own number of partners over a range of periods; these periods coincide with some of the time periods examined in other sexual behavior studies, including the number of partners over the past 12 months. This allowed us to put the behavior of the sample population into context. In calculating the mixing matrices, the data we used from patients with multiple sex partners violates the independence assumption used in deriving confidence intervals. Hence, in calculating the 95% confidence intervals for the probabilities, a model for correlated data (GEE) was used.23 These confidence intervals indicate the level of statistical error in our estimates, but it should be noted that they do not control for nonstatistical errors in responses.

Measures of the assortativeness of patterns of mixing. One way of categorizing patterns of mixing is by the level of assortativeness. Patterns can vary from people choosing partners who are similar (assortative), to partners being chosen at random (either with respect to their representation in the population in terms of numbers of persons or numbers of sex partnerships), to choosing partners unlike themselves (disassortative). In a mixing matrix, diagonal elements represent similar partnerships; therefore, the sum of the diagonal elements provides a crude but convenient quantitative measure of the degree of assortative mixing. Gupta et al12 used this observation to define a quantity, Q, to measure the degree of within group (assortative) mixing where (1)

Equation 1
Equation 1
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Here, ∑i = 1nwi is equal to the sum of the diagonal elements (i.e., the cells [1,1], [2,2] and [3+,3+] in the mixing matrix). In the assortative extreme, Q = 1; if mixing is random or proportionate, Q = 0; and in the disassortative extreme, Q = −1/(n − 1) for an n × n matrix (i.e., n sexual activity classes). The value of Q is calculated here for each of the mixing matrices. Because each element has a 95% confidence interval, it is possible to attach a degree of reliability to the estimate of Q. In effect, we are taking the sum of three proportions to derive the value of Q. If we assume that this sum is approximately normally distributed, then its 95% confidence interval is the sum ± z0.975 (∑i = 13 Si2), where si is the standard error of the ith proportion.

Comparing observed patterns with random patterns of mixing. The value Q provides a measure of how assortative the matrix is overall but fails to assess the contribution of individual probabilities and how they differ from random (in terms of the supply of sex partnerships). It would be possible to know how close to random each cell value was if we knew the “supply” of partnerships in the population as a whole. The population attending each clinic is not a closed population, so we must look at levels of sexual activity in the more general population. Unfortunately, the pattern of mixing derived in this study was based on the number of partners in a 3‐month period, whereas the NHSLS data for the U.S. population as a whole is based on a 1‐year period. However, for the respondents (but not the contacts) in this study, we had data on the number of partners over a period of 3 months and over a period of 1 year. By deriving a relationship between the numbers of partners reported by respondents for these two time periods in a Seattle population, we analyzed the NHSLS population to calculate a 3‐month distribution of activity for the “general” population. Specifically, for the entire sample in Seattle, we calculated the proportion of those with one partner for the past 12 months who had one partner in the past 3 months, the proportion of those with two to four partners for the past year who had one, two, or more than two partners in the past 3 months, and the proportion of those with five or more partners in the past year who had one, two, or more than two partners in the past 3 months. These proportions were then applied to the NHSLS data for the number of sex partners for the past year to calculate an expected distribution of the number of sex partners for the past 3 months. This allowed us to calculate further the proportion of sex partnerships “supplied” by each activity group (defined by number of partners in 3 months), which is the expected value for the probability of mixing “at random” for the activity group.

The NHSLS also presented proportions of respondents with a given number of sex partners for 1 year who reported the number of other sex partners they thought their partners had had in 1 year. From this estimate of the pattern of mixing according to partners in 1 year, estimates of the assortativeness of mixing could be derived. Sample sizes were not given so we could not attach levels of confidence to this estimate.

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Results

The frequency distribution of numbers of sex partners in 3 months stratified by sex is presented for the two clinics in Figure 1, as are the mean and standard deviation. The mean number of partners is highest for those attending the STD clinic. For those attending the Columbia Health Clinic, the mean numbers of partners are similar between sexes. However, there are differences in the variances of reported levels of activity, with women reporting greater heterogeneity at the Columbia Health Center. The pattern in each case is similar to patterns found in other studies of sexual behavior, except for the lack of a significant difference in reported numbers of sex partners between men and women. In other studies, there often has been such a difference,5,6,24 which is generally smaller for shorter time periods during which variation in behavior is more constrained. In the current study, the differences in the number of partners for 1 year were not statistically significant: Harborview STD clinic (sample A), mean for men 5.2, mean for women 3.7; (sample B), mean for men 4.2, mean for women 3.6; Columbia Health Center, mean for men 2.37, mean for women 2.57. A difference in mean number of sex partners would not be possible in a closed population with an equal sex ratio. That differences are not found here suggests that either biases in response to questions about sexual behavior are less pronounced among those attending clinics for reasons pertaining their reproductive health or that profiles of sexual behavior among attendees of STD clinics are more similar between men and women than they are in a random population sample.

Fig. 1
Fig. 1
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A comparison of the distribution of the number of sex partners in the past 12 months for the Seattle clinic populations with the study of the general population is made in Figure 2. It is immediately evident that there is a higher level of sexual activity in the STD clinic and the Columbia Health Center. The majority of persons in the NHSLS study had only one sex partner in the past 12 months. At the STD clinic, a much smaller proportion of men and women had only one partner; most reported three to four partners. Sample B (selected for ease of follow‐up) had a slightly different pattern from random sample A, with activity slightly lower in men and higher in women. The pattern for the Columbia Health Clinic is intermediate between that observed in the STD clinic and that observed in the NHSLS population.

Fig. 2
Fig. 2
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In the current study population of those with one sex partner in 1 year, 90% had one sex partner in the past 3 months. Of those with two to four partners in 1 year, 55.1%, 32.8%, and 6.5% had one, two, and three or more partners in 3 months. Finally, 22.4%, 28.3%, and 48% of those with five or more partners in the past year had one, two, and three or more partners in the past 3 months. Assuming that the relationship between the number of partners in 12 months and 3 months for the NHSLS is the same as that for the population in this study, we estimate that in the NHSLS population, 71.3%, 7.4%, and 3.6% of men and 73.1%, 38%, and 1.5% of women had one, two, and three or more partners, respectively, in 3 months. This estimate allowed the calculation of the expected values for the columns in Tables 2 and 3, in which, for random mixing, the probability of a partner from an activity group is the number of partnerships that group forms divided by the total number of partnerships. These values should be treated with some caution. STD clinic populations are likely to include many people whose levels of sexual activity are high for short periods of time (e.g., adolescents, those who have moved recently, or those subject to binge behavior). The relationship between the number of sex partners in 3 months and number of sex partners in 1 year is unlikely to be stable or simple. Undoubtedly, a sample of patients at STD clinics will differ from a sample of the general population. However, the only assumption made in calculating the expected random mixing patterns with which to compare our results was that people with high numbers of partners in 1 year were similar in both the NHSLS and the STD clinic samples. This would not be so if those likely to attend STD clinics were underrepresented in the NHSLS sample, because they would have been more likely to be homeless. Such a bias would lead us, when inferring 3‐month activity in the general population from the 3‐month activity in the Seattle clinic population, to overestimate the number of people with high numbers of partners in 3 months in the population as a whole. This, in turn, would cause the probability of forming a partner at random from those with only one partner to be underestimated and from those with three or more partners to be overestimated. Taking this potential bias into account would strengthen the interpretation of our results based on a comparison of the observed mixing pattern and that expected at random.

Table 2
Table 2
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Table 3
Table 3
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On separate analysis of the mixing patterns for both sexes at all three clinics, an interesting pattern emerges that is shared by all the mixing matrices. Figure 3 shows the matrix for male respondents in sample A at the Harborview STD clinic, which is typical of the pattern derived from all the study populations. Tables 2 and 3 present the matrices for each clinic along with 95% confidence intervals for each value. Everyone, regardless of number of sex partners, tends to report that one's partner had only oneself as a sex partner. Few patients reported that his or her partner had another sex partner. It is worth noting that those with three or more sex partners do report, significantly more often than would be expected by chance, that they think their partners have three or more sex partners. The interpretation of this pattern is complicated by the fact that the data are based on the respondent's knowledge of the sex partner's behavior, which often may not reflect the situation accurately. Based on the analysis of the NHSLS data, only in the case of respondents who report one partner are more contacts thought to have only one partner—cells (1,1) in Tables 2 and 3—than would be expected at random. This suggests that respondents are not overestimating the fidelity of their partners.

Fig. 3
Fig. 3
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In Figure 3b, the mixing matrix for the NHSLS population of male and female respondents is shown for comparison. The NHSLS mixing matrix has a different pattern from that seen in any of the Seattle clinic populations. There are still high values for cells (1,1) and (3,3). In fact, the values are slightly greater than those observed in Seattle. However, those with two or more partners seem to have partners who are less exclusive than those in the Seattle clinic samples. It should be remembered, though, that the duration was longer, which gives more time for a respondent's partners to have more than one partner. We note this comparison because of the scarcity of studies that have addressed such issues. However, the comparison is difficult to interpret because of the different time periods during which behaviors were assessed.

The pattern of mixing derived for respondents in sample B (selected for ease of follow‐up) at the Harborview STD clinic is slightly different from that of sample A (selected at random). All sample B male respondents had partners whose levels of sexual activity were higher than would have been expected with random mixing.

The values of Q with confidence intervals derived for each population are presented in Figure 4. Matrices are only moderately assortative. In each case, the major contributors to assortative mixing are those with one and those with three or more partners, i.e., positions (1,1) and (3,3). The significance of the level of assortative mixing to the epidemiology of a sexually transmitted infection depends on the biology of the infection. However, in general, highly assortative mixing makes an infection more likely to be established within a population but less likely to spread widely through the population.

Fig. 4
Fig. 4
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Discussion

In a separate publication based on the same study, Aral et al25 report on the concordance between sex partners assessed for a number of variables, including race, age, educational level, and number of partners. That analysis concentrated on mixing according to social characteristics and on how sex partner choice may operate in groups infected with different sexually transmitted infections. It was found that concordance according to lifetime numbers of partners was lower than concordance between individuals for attributes such as race and age. The focus was on attributes such as race, age, and social characteristics and the key role these factors play in the choice of sex partners. Societies are organized along such lines, and the risk of a person's acquiring an STD depends on the number of sex partners and on the prevalence of STDs in the groups from which those partners are chosen.25,26 By contrast, in the current study, we concentrated on the pattern of mixing based on the number of sex partners for a 3‐month period. At the population level, the number of infections in successive generations for a particular STD depends on the number of sex partners an infected person exposes and the number of sex partners the people they infect expose in turn. Thus, the prevalence of an STD in a social, cultural, economic, demographic, or geographic group is a product of the distribution of numbers of sex partners and of the pattern of mixing between those with a different number of partners, regardless of what motivates partner choice.12,19 In other words, the pattern of mixing according to sex partner numbers, measured here, should mediate the incidence of STDs, but it is itself an outcome of many other forces acting to determine partner choice. The number of partners is measured for a 3‐month period as it encompasses the incubation period for most bacterial STDs.

Our analysis revealed patterns of assortative (like with like) sexual mixing within the the heterosexual patient population. Those reporting high numbers of different sex partners in the past 3 months are more likely to select partners who also have high rates of sex partner acquisition than would expected if everyone had contact in direct proportion to the representation of the different activity classes in the sample (Tables 2 and 3). As summarized in Figure 4, the degree of assortativeness or concordance is low to moderate in all the clinics and in both sexes (reflected by the magnitude of the statistic Q, which characterizes the structure of the recorded mixing matrices). On examining the elements of the eight matrices (two per clinic, reflecting males and females), it becomes clear that many of the sexual contacts made by the patients are not part of the clinic population because the proportion of contacts with no other partner is far in excess of the proportion of the clinic sample that reported only one partner. Obviously, therefore, those attending the three study clinics are a biased sample of the population of Seattle, as is suggested by Figure 2, and their sex partners also make up a different sample. This is to be expected, because network studies of this kind are rarely if ever “closed” (i.e., not all contacts are made within the sample).

The reliability of information from the index patient and contacts can be questioned, and many index patients have incomplete knowledge of their partners' behavior. It is of great interest to compare the responses of index patients with responses of their contacts; this issue will be addressed in a subsequent publication.

Particular features of the recorded mixing matrices (see Figure 3a) are the high values of the elements in positions (1,1) and (3+,3+), the low values in position (2,2), and the high values in the first column reflecting contacts of respondents of all three activity levels (one, two, and three or more sex partners in the past 3 months) with persons they believe have only one partner over the same time interval. The first observation (i.e., high values in 1,1 and 3,3) is to be expected in the assortative case, and these high values have a strong influence on the overall degree of assortativeness of the matrices. The low values in the 2,2 position and the high values in the 2,1 and 3,1 positions are indications of disassortative mixing. They act to keep the overall value of Q in the region of low to moderate assortativeness or concordance.

Bearing in mind the unknown bias of the estimates of assortativeness, a degree of reliance in these qualitative conclusions can be derived from the similarities in the patterns from the two clinic settings and in each sex (see Figure 4). The major implication of this pattern is related to the prevalence of STDs in Seattle. Just as infection is found to concentrate within geographic areas,27 it tends to be maintained in the high activity core group—i.e., those with more than three sex partners—particularly in those who have high‐activity partners. However, because the degree of concordance by sexual activity is generally low to moderate, the high‐activity persons will transmit infection persistently (if infrequently) to the groups with lower activity. This is a demonstration of the potential importance of the core group in the persistence of STD infection within the general population of sexually active persons.

A reflection of the attributes that characterize the core is provided by comparing the frequency distributions of the reported sex partners over a defined period of time in the NHSLS population7 with those recorded in the STD clinics (Figure 2). It is clear that the study samples represent a high activity group and that they constitute a small fraction of the total population of sexually active persons who typically report few sex partners. Within the study population attending clinics for STD diagnosis and treatment, there is a small subsection who have very high activity and who have partners with very high activity. This subsection can be thought of as the essential core for STD persistence. Mixing in the NHSLS sample was much more assortative, with many people in seemingly exclusive sex partnerships. However, the less assortative mixing in STD clinic populations suggests that the 17% of the NHSLS population with more than one partner does expose itself to some risk of infection with STD.

The small number of people with three or more partners who also have three or more partners suggests that STD treatment targeted to this group could have a significant impact on the ability of bacterial STDs to persist in the Seattle population. However, for chronic persistent STDs such as genital herpes, human papilloma virus (HPV) infection, hepatitis B, and HIV infection, the observed pattern is worrisome. If these infections manage to invade this small group of high‐activity heterosexuals, over time they are likely to spread heterosexually through a substantial portion of the population (i.e., those with lower activity with whom they mix). In fact, the extraordinarily high prevalences of HSV2 and HPV infections in the general U.S. population (approximately 20% to 25% for HSV2 and 20% to 40% for HPV; see, for example28,29) suggest that this has already begun and that it continues, further raising the concern regarding HIV transmission. In further articles based on studies of sexual mixing patterns, we will examine the impact of the patterns on STD transmission and control in mathematical models and analyze sexual networks in the context of socioeconomic, behavioral, and demographic factors.

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References

1. Kinsey AC, Pomeroy WB, Martin CE. Sexual behaviour in the human male. Philadelphia: WB Saunders, 1948.

2. Kinsey AC, Pomeroy WB, Martin CE, Gebhard PH. Sexual behaviour in the human female. Philadelphia: WB Saunders, 1953.

3. Reinisch JM, Ziemba-Davis M, Sanders SA. Sexual behaviour and AIDS: Lessons from art and sex research. In: Voeller B, Reinisch J M, Gottlieb M, eds. AIDS and Sex: An Integrated Biomedical and Biobehavioural Approach. Oxford: Oxford University Press, 1990:37–80.

4. Cochran WG, Mostelier F, Tukey JW. Statistical problems of the Kinsey Report. J Am Stat Assoc 1953; 48:673–716.

5. Johnson AM, Wadsworth J, Wellings K, Bradshaw S, Field J. Sexual lifestyles and HIV risk. Nature 1992; 360:410–412.

6. Analyse des Comportements Sexuels en France Investigators. AIDS and sexual behaviour in France. Nature 1992; 360:407–409.

7. Laumann EO, Gagnon JH, Michael RT, Michaels S. The social organization of sexuality: Sexual practices in the United States. Chicago: The University of Chicago Press, 1994.

8. Hethcote HW, Yorke JA. Gonorrhea transmission dynamics and control. Vol. 56. Lecture Notes in Biomathematics. Berlin: Springer-Verlag, 1984.

9. Anderson RM, Medley GF, May RM, Johnson AM. A preliminary study of the transmission dynamics of the human immunodeficiency virus (HIV), the causitive agent of AIDS. IMA J Math Appl Med Biol 1986; 3:229–263.

10. Anderson RM, May RM. Epidemiological parameters of HIV transmission. Nature 1988; 333:514–522.

11. Jacquez JA, Simon CP, Koopman J, Sattenspiel L, Perry T. Modeling and analyzing HIV transmission: The effect of contact patterns. Math Bio 1988; 92:119–199.

12. Gupta S, Anderson RM, May RM. Networks of sexual contacts: Implications for the pattern of spread of HIV. AIDS 1989; 3:807–817.

13. Anderson RM, Gupta S, Ng WT. The significance of sexual partner choice networks for the transmission dynamics of HIV. J AIDS 1990;3:417–429.

14. Morris M. A log-linear modeling framework for selective mixing. Math Bio 1991; 107:349–377.

15. Hogsborg M, Aaby P. Sexual relations, use of condoms and perceptions of AIDS in an urban area of Guinea-Bissau with a high prevalence of HIV-2. In: Dyson T, ed. Anthropological and Socio-Cultural Studies in the Transmission of HIV. Liege, Belgium: Ordina Publications, 1992:29–36.

16. Haraldsdottir S, Gupta S, Anderson RM. Preliminary studies of sexual networks in a male homosexual community in Iceland. J AIDS 1992; 5:374–381.

17. Rothenberg RB. The geography of gonorrhea: Empirical demonstration of core group transmission. Am J Epidemiol. 1983; 117:688–694.

18. Potteratt JJ, Rothenberg RB, Woodhouse DE, Muth JB, Pratts CI, Fogle JS. Gonorrhoea as a social disease. Sex Transm Dis 1985; 12:25–32.

19. Garnett GP, Anderson RM. Contact tracing and the estimation of sexual mixing patterns: The epidemiology of gonococcal infections. Sex Transm Dis 1993; 20:181–191.

20. Anderson RM, May RM, Ng WT, Rowley JT. Age-dependent choice of sexual partners and the transmission dynamics of HIV in sub-Saharan Africa. Philos Trans R Soc Lond Biol. 1992; 336:135–155.

21. Granath F, Giesecke J, Scalia-Tomba G, Ramstedt K, Forssman L. Estimation of a preference matrix for women's choice of male sexual partner according to rate of partner change using partner notification data. Math Bio 1991; 107:341–348.

22. Hsu Schmitz SF, Castillo-Chavez C. Parameter estimation in non-closed social networks related to dynamics of sexually transmitted diseases. In: Kaplan EH, Brandeau ML, eds. Modeling the AIDS epidemic: Planning, policy and prediction. New York: Raven Press, 1994:533–559.

23. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13–22.

24. Morris M. Telling tails explain the discrepancy in sexual partner reports. Nature 1993; 365:437–440.

25. Aral SO, Hughs J, Stoner B, Whittington W, Holmes KK. Demographic and behavioural concordance between sex partners: Partnerships infected with chlamydia trachomatis are different than those infected with gonorrhea and syphilis. In: Orfila J, Byrne GI, Chernesky MA, et al, eds. Chlamydial Infections: Proceedings of the Eighth International Symposium on Human Chlamydial Infections. Chantilly, France, 19–24 June, 1994. Bologna, Italy: Societa Editrice Esculapio, 1994:17–20.

26. Garnett GP, Anderson RM. Sexually transmitted diseases and sexual behaviour: Insights from mathematical models. J Infect Dis. 1996; in press.

27. Rothenberg RB, Potterat JJ. Temporal and social aspects of gonorrhea transmission: The force of infectivity. Sex Transm Dis 1988; 15:88–92.

28. Bauer HM, Hildesheim A, Schiffman MH, et al. Determinants of genital human papillomavirus infection in low-risk women in Portland, Oregon. Sex Transm Dis 1993; 20:274–278.

29. Nahmias AJ, Lee FK, Beckman-Nahmias S. Sero-epidemiological and -sociological patterns of herpes simplex virus infections in the world. Scand J Infect Dis 1990; 69(suppl):19–36.

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