Estimates of age-specific partnership formation rates among urban men who have sex with men Sara J. Nelson, MPH University of Washington, Seattle Department of Epidemiology Center for AIDS and STD March 12, 2008 Thank you Dr. Wohlfeiler and thanks to the conference organizers to giving me the opportunity to present these data.

Background Men who have sex with men (MSM)  4% of US men = 71.5% of new US male HIV cases (CDC, 2005) = 64% of all US P&S syphilis cases (CDC, 2006) Partnership formation rates are primary determinants of HIV/STD transmission dynamics We previously estimated rates for heterosexuals Men who have sex with men (or MSM) represent a disproportion burden of STD and HIV in the United States. Approximately 4% of US men are MSM, yet MSM represent over 70% of recent HIV cases among US men and 64% of all recent primary and secondary syphilis cases. Both biological and behavioral factors affect HIV and STD transmission dynamics. In this talk, I’ll focus on an important behavioral determinant, the rate at which new sexual partnerships form. These data are both useful to mathematical modelers but may also help us understand the tremendous disparities in HIV and STD rates between MSM and other populations, as well as within the MSM population. We became interested in calculating these rates for MSM after conducting a similar analysis among heterosexuals where we estimated partnership formation rates using cross-sectional data. HIV (33 states), Syphilis (30 states) 1General Social Survey 2004; 2CDC, HIV/AIDS Surveillance Report 2005; 3Beltrami JF, ISSTDR 2007.

Annual partnership formation rate Age-specific partnership formation rates among heterosexual men and women Proportion (%) Annual partnership formation rate We calculated partnership formation rates among heterosexual adults, age 18-39, using a random digit dial survey in Seattle. We found that at every age, less than 50% of adults reported forming a new partnership in the past year. Indeed, in their 30s, less than 20% formed a new partnership in the preceding year. When I plot the annual partnership formation rate, you see consequently that this rate peaks around age 19 or 20 and declines steadily to about 0.4 partnerships or less per year by age 35. Given the profound disparities in HIV and other STD rates between MSM and heterosexuals, we sought to repeat these analyses among MSM to explore if their patterns of partnership formation could help explain some of these disparities. (Not sure I like how this is framed…) Age

MSM Partnership Formation Using data from a large and representative cross-sectional survey Challenge #1: Few describe their entire lifetime sexual history using typical egocentric partner methods Restrict to data from past 1 year, 5 years Challenge #2: Potential cohort effect Compare contemporary data to earlier cohorts Compare to a more recent data source But among MSM, calculating these rates becomes more complicated. In the absence of longitudinal data with complete enumeration of partnerships, we try to exploit large and representative cross-sectional datasets. However, as we discovered this presents two principle challenges when attempting to construct partnership formation rates. First, we are most interested in the lifecourse of partnership formation rates and how these may affect disease transmission. In our heterosexual analyses, where participants were asked detailed questions about their 5 most recent partners, this resulted in 45% of participants describing their entire sexual history. Among the others, we assumed that all partnerships not described in the survey occurred between sexual debut and the earliest described partnerships. However, with samples of urban MSM, few are able to describe their entire lifetime sexual history using egocentric data methods typically used in surveys since the mean number of lifetime partners is generally higher. Thus it’s difficult to make the same assumption that all non described partnerships were equally distributed between sexual debut and the present, particularly when this is over the span of many years. Therefore, we chose to derive partnership formation rates throughout the lifecourse only using data about partnerships in the past year and past 5 years. The second challenge is the potential for a cohort effect. In other words, do age-specific partnership rates vary across birth cohorts? Or can we assume that the “natural history” (if you will) of partnership formation patterns is the same among MSM born at different times? We explored two possible ways of answering the question about whether or not we need to account for cohort effects in constructing partnership formation rates. First, we could compare recent partnership formation rates in one cohort to earlier rates in an older cohort. The second way is to compare our findings with a more recent data source.

Study Objective To estimate age-specific partnership formation rates among a population-based sample of urban MSM Therefore, the overall primary aim for this study was to estimate age-specific partnership formation rates using a population-based dataset of urban MSM, and in doing so, trying to address the challenges I just described.

Study Populations & Surveys Urban Men’s Health Study (UMHS, n=2,881) 1996-1998 RDD (NYC, LA, SF, CHI) MSM age ≥18 Any same-sex sexual behavior since age 14 Self-identify as gay, homosexual, or bisexual Variables # of partners since age 18, in past 5 years, in past year Detailed data on up to 4 partners in past year Age at sexual debut Other demographic data 2 Seattle MSM Random Digit Dial Surveys (n=400 each) 2003 & 2007 RDD (3 Seattle ZIP codes) # partners in past year For the bulk of these analyses I used data from the Urban Men’s Health Study. This was a large cross-sectional, random-digit-dial telephone survey conducted between 1996 and 1998 in 4 US cities – New York, Los Angeles, San Francisco, and here in Chicago. Men in randomly called households were eligible if they were at least 18 years old and either reported same-sex sexual behavior since the age of 14 or self-identified as gay, homosexual, or bisexual. The RDD survey contained modules on recent sexual behavior, HIV, and other STDs. For these analyses we focused on variables pertaining to the number of partners. These included the number of partners in the past 5 years and in the past year. We also used detailed partnership data from up to 4 partners in the past year, age at sexual debut, and demographic data such as age. We limited our analyses to MSM who had complete data for each of these variables. Because the UMHS is now 10 years old, I also used data from two Random Digit Dial surveys of Seattle MSM, conducted in 2003 and 2007. These data helped us explore potential cohort effects. The Seattle RDDs have similar inclusion criteria and randomly called households in 3 ZIP codes with known high proportions of MSM. For my comparison analysis, I used a single variable – the reported number of partners in the past year.

Defining Partnership Start Ages time Age - 5 Age - 1 Age # partners in last 5 years – # partners in last year # partners in last year In order to estimate partnership formation rates at each age, we had to estimate the number of new partnerships each man formed during each year of his life. We looked at both the past year and the past 5 years. To do this, we created 2 windows of time: the number of partners during the 1 year prior to the interview and the number of partners in the prior 5 years minus the number in the past year.

Defining Partnership Start Ages time Age - 5 Age - 1 Age # partners in last 5 years – # partners in last year # partners in last year These data also included detailed partnership data for up to 4 recent partners in the past year. A question for each of these partners asked if the partnership started more than a year ago. If this was indicated, we did not include that partner in the count for new partners in the past year. Instead, we assumed that the partner was included in the previous time window. Finally, we assumed that partnerships were distributed evenly within each of the 2 windows. After doing this, we could approximate the number of partnerships begun at each age.

# person-time observed at age Analysis Age-specific partnership formation rate: # partners begun at age # person-time observed at age We calculated partnership formation rates by dividing the total number of partnerships begun at an age, or age range, by the total amount of person-time observed at that age. For these analyses, we assumed that person-time for each participant began accruing at age 18 and ended at the midpoint of their age at interview.

Participant Characteristics UMHS n(%) Age (mean [SD]) 39 [11.2] MSM of color 527 (20.6) ≥ College Degree 1820 (71.3) City: San Francisco 814 (31.9) NYC 709 (27.8) Los Angeles 658 (25.8) Chicago 372 (14.6) Born: <1940 188 (7.4) 1940-49 352 (13.8) 1950-59 751 (29.4) 1960-69 951 (37.3) 1970-79 311 (12.2) RDD Participants N=2,881 Incomplete Data n=329 Analytical Sample n=2,552 From the 2,881 UMHS participants, we excluded 329 men who were missing at least one of the variables used in our analyses, leaving 2,552 for this study. Among the UMHS sample, the average age was 39 years, about 20% were men of color, and over 70% had at least a college degree. There was a slight difference in the proportion of men from each city. Over half of the men were born in the 1950s and 1960s, with smaller fractions of men born in earlier and later cohorts. (?) Finally, 16.4% of MSM reported unprotected anal intercourse with ejaculation, either receptive or insertive, with any male partner in the past 12 months.

Results: Number of Sex Partners in the Past Year among UMHS MSM Range: 0-500 Median = 3 Mean = 11 Proportion (%) First, some descriptive data about numbers of sex partners. This histogram plots the distribution of the number of sex partners in the past year among MSM in this sample. For this plot, we capped the variable at 50, and you can see that the distribution has a strong positive skew. This is also born out comparing the median and mean. The median number of partners in the past year was 3, while the mean was 11. A quarter of MSM reported just one sex partner in the previous year.

Results: % of UMHS MSM with ≥1 new partnership in past year Proportion (%) On this chart I present the proportion of MSM at each age who reported at least one new partnership in the preceding year. At the youngest ages, close to 100% of MSM reported at least one new partner in the past year. This proportion declined through the 20s, 30s, and 40s, but remained at a long plateau just below 75%. Even through their 50s, at least half of MSM reported forming a new partnership in the past year. Age at interview

Partnership formation rate Results: Age-specific partnership formation rates in the past year among UMHS MSM Partnership formation rate The slide includes our primary findings. These are the partnership formation rates in the last year among MSM in roughly 5 year age ranges. The rates among the youngest two age groups, ages 18-29, start at between 9 to 10 partnerships per year, the peak partnership formation plateaus among the 30-44 year olds at approximately 12 to 14 partnerships per year. Then the partnership formation rates drop to between 7 to 8 partnerships per year in the late 40s and 50s. This pattern is a marked contrast to what we saw among heterosexuals, who peaked in the earliest age group and then realized a steady decline through adulthood. Age at interview

Results: Median Age-Specific Partnership Formation Rates in Past Year among UMHS MSM 75th %ile Median Number of Partners 25th %ile Because number of partners has such a non-normal distribution, we also looked at median number of new partnerships formed in the past year. The most informative way to present these data is to use a box plot. So to remind you -- the line inside the blue box is the median number of partners, the top and bottom lines of the box are the 75th and 25th percentiles. The lines extending from these boxes represent the effective range. As we’d expect, the median number of partnerships is much lower than the mean, at less than 5 for each age group. However, the general pattern is fairly similar -- with a peak in the early 30s. Age at interview

Exploring Potential Cohort Effects: Comparing to Previous Cohort Age at Interview Ages covered by partnership formation rates in past 5 years Age at Interview of group with comparable partnership formation rates in past 1 year 25-29 year olds 21-29 21-29 year olds 30-34 26-34 35-39 31-39 40-44 36-44 45-49 41-49 50-54 46-54 55-59 51-59 Now I’ll explore potential cohort effects in partnership formation rates. In a previous slide I presented partnership formation rates among these age groups using new partnerships from the past year. To explore potential cohort effects, we used data from the past 5 years to construct new rates. Thus, among the 35-39 year age group, data from the previous 5 years provided estimates for ages 31-39. We then compared the rates estimated using 5 year data to rates estimated using 1 year data among an age group analogous to the age range covered by the 5 year data. So, we compared the 5 year data among the 35-39 year olds to the 1 year data of 31-39 year olds. Therefore, if the rates are relatively similar, we can assume no secular change in rates, at least in the past 5 years.

Exploring Potential Cohort Effects: Comparing to Previous Cohort Partnership formation rate This chart presents those comparisons. First, in blue, I plotted the partnership formation rate using data on partners in the past 5 years among 5 year age groups. Now, in green, I plotted the partnership formation rate in the past year among the cohort with analogous 1 year data. There is not a clear, consistent difference between the rates across cohorts. These data, therefore, do not provide evidence of a secular trend, at least between consecutive birth cohorts Age at interview

Exploring Potential Cohort Effects: Using an Alternative Dataset Partnership formation rate An alternative way to explore potential cohort effects is to use an alternative data source. In this case, we compared the number of partnerships formed in the past year between the UMHS data and the 2 Seattle RDDs combined. At most ages the UMHS participants (in blue) had much higher partnership formation rates than the Seattle MSM (in yellow). However, the shape of partnership formation across age groups is relatively similar to what we found using the UMHS data. Since Seattle was not included in the UMHS data, we cannot say whether the change in partnership formation was a true change in rate over the past 10 years, or if these results merely reflect differences in populations. Age at interview

Summary Sustained partnership formation rates throughout adulthood among MSM with peak formation rates in the 30s and early 40s. Over 50% forming new partnerships at all ages Did not see clear evidence of a short term cohort effect In summary, our findings suggest sustained partnership formation rates throughout adulthood among MSM with a long plateau between the ages of 30-44. Indeed, over half of MSM continued to form new partnerships at all ages. We also explored potential cohorts effects, but our data, which had a limited ability to address this question, did not provide clear evidence of a secular trend in partnership formation rates, at least within the past 5 to 10 years.

Limitations Cross-sectional data UMHS conducted 1996-1998 (survivor bias) Aggregate data Misclassification Partnerships may be counted twice Includes all sexual partnerships UAI most relevant (?) Still not ideally parameterized Our study had the following limitations. First, I have already highlighted several of the methodological limitations with cross-sectional data, namely the inability to combine data across age groups in the presence of a secular trend. Second, the Urban Men’s Health Study was conducted between 1996 and 1998. Many high risk MSM from early birth cohorts may have died from HIV/AIDS or other causes, resulting in survivor bias. Third, because we only had detailed partnerships on just a few recent partners, we had to use aggregate partnership data for most of our analyses. Obviously, there is likely some misclassification. Also, our methods may overestimate true partnership formations rates if some partnerships bridged the different time periods that our variables measured and were counted twice. We accounted for this as much as the data would allow. Fourth, these estimates include ALL sexual partnerships, including those with women and all types of sexual activity. Anal sex partnerships with men, specifically those in which the anal sex is unprotected, are likely the most applicable from a disease transmission viewpoint. The structure of these data doesn’t allow us to know about condom use rates in earlier partnerships. (?) Finally, we acknowledge that these rates alone do not provide all of the information to comprehensively assess partnership formation patterns. So what would we want? Ultimately I think we’d like to measure not just the timing, but also the number of sexual acts within a partnership, sexual role, and condom use within all partnerships. Our findings show what we can get from a single cross-sectional study. Serial cross-sectional studies could provide more. And clearly, long-term, prospective data with enumeration of all partnerships are needed to calculate the most valid estimates.

Conclusions Sustained formation rate pattern consistent with late peak incidence of HIV Helpful for age-structured mathematical models Implications for other viral infections Need for lifelong prevention messages Need longitudinal data with complete partnership enumeration In conclusion, our findings of sustained partnership formation rates throughout adulthood are consistent with the late peak incidence of HIV among MSM. This is in contrast to the pattern we observed among heterosexuals who tended to peak much earlier and have a significant reduction in new partnership formation throughout adulthood. Nevertheless, the relative magnitude of this relative to other behavioral and biological factors is uncertain. On the research side, our age-specific rates should be helpful to researchers employing age-structured mathematical models. On the program side, our findings have the strongest implication for other viral infections such as HIV and herpes. For these infections, prevalence tends to rise with age, thus sustained partnership formation rates demonstrate the need for prevention messages to be directed to all MSM, even well into advanced adulthood. And finally, our study highlights the methodological challenges of cross-sectional data. Clearly, longitudinal data with complete partnership enumeration, over a long period of time are necessary to fully parameterize partnership formation rates among MSM.

Thank you! UMHS participants UMHS investigators UW Center for AIDS & STD Matthew R. Golden, MD MPH Sara J. Nelson, MPH University of Washington Center for AIDS and STD sjnelson@u.washington.edu