The objective of this study was to determine whether the prevalence of STIs among the available pool of sex partners in a neighborhood, measured indirectly, is an independent determinant of a current incident STI. The target population Calculate weighted average statistics of sexual partners 58, English-speaking, sexually active 15—24 year olds in census block groups CBGs in Baltimore, MD.
A sample of 65 CBGs was selected using a stratified, systematic, probability-proportional-to-size strategy and 13, households were randomly selected. From —, research assistants administered an audio-CASI survey and collected biologic samples for gonorrhea and chlamydia testing. The final sample size included participants from 63 CBGs. Additional data provided gonorrhea prevalence from — per 15—49 year olds perper CBG. After adjusting for individual-level STI risk factors in a multilevel probability model, adolescents and young adults
Calculate weighted average statistics of sexual partners in high vs.
To inform prevention programs, future research should focus on identifying mechanisms through which context causes changes in local sexual networks and their STI prevalence.
Sexually transmitted infection STI transmission and acquisition models propose and research shows that incident infections are due in part to demographic factors age and gender and sexual behaviors condom nonuse and number of sex partners Figure 1 1 — 5.
The natural and likely assumption is that these demographic factors and sexual behaviors explain in part the persistence of STIs in geographic areas 6 — 9.
For example, a high STI area may contain a greater proportion of adolescent girls or a greater proportion of people who do not use condoms.
This would that neighborhood of residence is just a proxy for individual characteristics of the residents and not in of itself an independent risk factor. Hypothesized pathways through which historically high prevalence sexually transmitted infection STI areas relate to current incident STIs and ultimately currently high prevalence STI areas. STI transmission and acquisition models, however, also propose that incident STIs are related to exposure to infected sex partners Figure 1.
As such, it can be argued that another independent risk for STIs may be the prevalence of STIs among the available pool of sex partners in a neighborhood 39 — For example, some geographic areas may have a higher incidence of infection because they have a greater proportion of infected sex partners within the local available sex partner pool.
This hypothesis assumes that local sex networks are at least in part formed on the basis of propinquity, i. The objective of this study was to determine whether the prevalence of STIs in the available pool of sex partners in a neighborhood Calculate weighted average statistics of sexual partners an important determinant of incident STIs.
Since the prevalence of infection in the available sex partner pool could not be measured directly, we used an indirect approach to assess the association between infection in neighborhood sex partner pools and individual-level risk for STIs.
In Baltimore there are with historically higher and lower prevalence and presumably incidence of STIs. In this study, we selected a random sample of census block groups, our indicator of neighborhood, which varied in their historical prevalence of STI. We then recruited a random household sample of adolescents and young adults from each block group and tested whether the likelihood of a participant having a current incident STI was associated with the prevalence of STIs in their neighborhood.
And then finally, we tested whether this association persisted after adjusting for individual-level STI risk factors of the participants in each CBG. To the extent that the association persists, we argue that geographic variation in the available pool of sex partners is associated with individual-level risk of an STI. The current study Calculate weighted
Calculate weighted average statistics of sexual partners statistics of sexual partners conducted in Baltimore City, Maryland.
Baltimore has a history of consistently high gonorrhea prevalence. Init ranked ninth in gonorrhea prevalence perpopulation among counties and independent cities in the U. Data for this study were collected from several sources. We obtained data from a household study which is Calculate weighted average statistics of sexual partners below. We also obtained public health surveillance data from Calculate weighted average statistics of sexual Baltimore City Health Department to measure gonorrhea prevalence among 15—49 year olds perper
Calculate weighted average statistics of sexual partners for the period — The household study was conducted from April Calculate weighted average statistics of sexual partners April The target population included English-speaking, sexually-active persons between the ages of 15 and 24 years who resided in CBGs.
We estimate that the target population comprised approximately 58, persons living in the CBGs in The sampling selection for the study was conducted in two stages. This subsample was selected for two reasons: Gonorrhea prevalence was generated from public health surveillance data among 15—49 year olds perper CBG from — Estimates of eligible households were generated using Census information The CBGs were then placed into primary strata by deciles of gonorrhea prevalence, and ordered by the percent of households below the Federal poverty line and by geography.
A final sample of 65 block groups was selected using a stratified, systematic probability proportional to size sampling strategy, where size was defined by the estimated number of eligible households Figure 2. In the second sampling stage, we obtained address Calculate weighted average statistics of sexual partners from three different vendors for the 65 selected block groups to create a household sampling frame.
A total of 27, addresses associated with the 65 selected block groups served as the second-stage sampling frame. We then used non-linear optimization to allocate a sample of 13, households to the three lists in a way that reduced screening costs while controlling for design effects Our target enrollment for each block group was 10 participants based on Optimal Design power and sample size calculations All sampled households received a lead letter describing the study approximately two weeks before the households were contacted for enumeration.
Screening was conducted to determine eligibility.
In selected households with more than one age-eligible person, one was randomly selected for screening. If eligible and willing to participate, consenting individuals were enrolled and research assistants administered an audio computer-assisted self-interview audio-CASI in a private setting.
The audio-CASI survey captured information on demographics as well as sexual Calculate weighted average statistics of sexual partners information including individual- and partner-related sexual histories and risk behaviors. For example, we asked each individual whether they had ever been infected with gonorrhea, chlamydia and other specific STIs. We also asked number
Calculate weighted average statistics of sexual partners sex partners in their life and in the past 90 days and whether they had ever had sex with an HIV-infected individual or an injection drug user.
For each sex partner named in the last 90 days we the same if not similarly specific questions. Self-administered vaginal swabs for females and urine samples for males have been shown in previous research to be feasible and acceptable methods for collecting biologic samples for STI testing and to have high sensitivity and specificity with NAAT 19 — Gonorrhea prevalence was selected Calculate weighted average statistics of sexual partners the indicator of STI prevalence because of the standard reporting procedures for and the relatively large number of cases reported in any one year, thus providing
Calculate weighted average statistics of sexual partners estimates of disease prevalence.
We chose not to use reported cases of chlamydia in our indicator of STI prevalence because of the ascertainment bias associated with Calculate weighted average statistics of sexual partners surveillance. Individual-level demographic information and a well-established, relatively complete list of individual-level behavioral STI risk factors were utilized to adjust the regression models for the most proximal individual-level factors associated with a current incident STI and to determine the independent association of gonorrhea prevalence as a marker for STI infected sex partner pools.
We chose our individual level factors based on STI transmission and acquisition models. In these models, the most proximal risk factors for acquisition are efficiency of transmission, sexual behaviors, and probability that a sexual contact is infected, i. Since the purpose of this study was to examine the effect of prevalence in STIs in available sex partner pools, we chose to control for those demographic factors associated with efficiency of transmission - age continuousgender male, female - and sexual behaviors - condom use yes, no and number of recent sexual contacts partners in the past three months 0,1, 2, 3 and greater than or equal to four sex partners.
All other known risk factors for STIs are only markers for these proximal risk factors. Statistical analyses included the calculation of statistical analysis weights, and weighted and unweighted summary statistics. We also conducted exploratory analyses culminating in the generation of a series of multilevel probability models.
Multilevel models represent the most appropriate method of analysis as the data form a nested data structure; i. Multilevel analysis accounts for the non-independence of observations within groups, uses empiric Bayes adjustments for the group means and allows for statistical testing of the and within group variances on the outcome, current incident STI. All analyses were conducted using Stata, version 9. Statistical analysis weights enable design-consistent estimation of population parameters by adjusting for disproportionate characteristics between sample members and the target population.
In this study weights were generated to reflect the unequal probabilities Calculate weighted average statistics of sexual partners selection of an individual and a CBG and to adjust for potential biases attributable to differential response and coverage between sample members and the target population.
In multilevel analysis, the sampling need to be constructed differently than the sampling weights for single-level or population-average models. A common approach and the one utilized in our analyses is a method of computation devised by Pfefferman et al.
To calculate weighted and unweighted response rates for both the interview and the collection of a biologic specimen, we used the operational definitions and formulas for in-person household surveys described by the American Association of Public Opinion Research Specifically we used the
Calculate weighted average statistics of sexual partners RR3 which uses the known eligibility rate to pro-rate eligibility among cases with unknown eligibility.
We used the RR3 formulation because there were no partial interviews and because most of the addresses with unknown eligibility were occupied housing units and at least some were likely to have eligible persons. Exploratory analysis was conducted and summary statistics were generated for the individual-level variables and census block group-level variables. A series of multilevel Calculate weighted average statistics of sexual partners models were generated to determine if and the extent to which the prevalence of gonorrhea, as a marker for infected sex partner pools, at the census block group-level was significantly associated with a current incident STI after adjusting for the most relevant individual-level demographic and behavioral STI risk factors including age, gender, condom use at last sex and number of sex partners in
Calculate weighted average statistics of sexual partners last 90 days.
In all models two requirements were used for statistical significance including a confidence interval that did not include 1. First, an unconditional multilevel model was used to assess the extent of variation in current incident STIs between the communities. Then individual-level variables were added to this model to determine the extent to which the individual-level variables were significantly associated with a current incident STI.
Subsequently, models were generated to assess the independent relationship between gonorrhea prevalence Calculate weighted average statistics of sexual partners the CBG level and current incident STI. Finally, all individual-level and CBG gonorrhea prevalence were entered into a multilevel model to assess the independent relationship of gonorrhea prevalence after adjusting for the individual-level factors.
For ease of interpretation, we conducted all models which gonorrhea prevalence in two ways -- one, with gonorrhea prevalence as a continuous variable and two, with gonorrhea prevalence as a dichotomous variable.
We also conducted all analyses using weighted and unweighted data. During the screening, two of the 65 CBGs were found to be comprised exclusively of retirement communities and thus were excluded. One age-eligible person Calculate weighted average statistics of sexual partners randomly selected for screening from each household.
The final sample size at the individual-level was The number of individuals within a CBG ranged from 1 to 23 mean The weighted and unweighted statistics were quantitatively and qualitatively similar so we present only the weighted results. The summary statistics were as follows: the neighborhood-level, the average overall gonorrhea prevalence per CBG was 1, The mean current incident STI was 6.
Next we generated a multilevel model to confirm the expected association between individual-level factors and individual-level current incident infection accounting for the clustering of participants within CBGs Table 2model 1. As expected, younger age, female
Calculate weighted average statistics of sexual partners, higher numbers of sex partners in the past 90 days and condom nonuse were all associated with increased likelihood of a current incident STI although only age was significantly associated.
Next in multilevel models, we ascertained the relationship between the gonorrhea prevalence in two separate models as continuous results not shown and dichotomous and current incident STI Table 2model 2. In both models, increased gonorrhea prevalence at the level was significantly associated with an increased likelihood of an individual-level current incident STI.
Specifically, individuals in high gonorrhea prevalence areas compared to individuals in low prevalence areas were 26 times more likely to be diagnosed with a current STI weighted odds ratio OR In the final multilevel models, all individual-level factors and gonorrhea prevalence measured in two ways were entered into the models Table 2model 3.
The link between the likelihood of a Calculate weighted average statistics of sexual partners incident STI and gonorrhea prevalence decreased but remained highly significant after controlling for the most relevant individual-level STI risk factors weighted OR 4.
In Calculate weighted average statistics of sexual partners final model, gonorrhea prevalence helped to explain an additional 12 percent of the level two variance associated with a current incident STI.
The current study finds that the gonorrhea prevalence in areas is independently associated with a current incident bacterial STI after controlling for individual-level STI demographic and behavioral risk factors. To the extent that our measure of gonorrhea prevalence after controlling for individual STI risk factors represents the infected pools of available sex partners, we argue that geographic variation in STI infection in the available pool of sex partners is associated with individual-level risk of an STI.
The findings fill a critical gap Calculate weighted average statistics of sexual partners a growing body of research recognizing the limitations of research where individual-level STI risk factors alone explain increased risk for STIs 529