The annual human immunodeficiency virus (HIV) diagnosis rate in the United States declined by 33.2% from 2002 to 2011,1 and by 5% from 2011 to 2014.2 This overall change has been paralleled in other high-income countries (for example, the number of new diagnoses in the United Kingdom fell by 20% between 2005 and 2012). Among 58 low-income and middle-income countries assessed in the Joint United Nations Programme on HIV/AIDS Global Report for 2012,3 49 had decreasing (39) or stable (10) rates and 9 exhibited increases between 2001 and 2011.3 Although this overall change hides important variation by age, sex, race, and risk group, with considerable increases among some subgroups and within some countries, the era of unfettered epidemic spread of HIV appears to be over. As we enter a phase of endemic transmission, understanding the new dynamics is of increasing importance.
Some years ago, we postulated that endemic transmission of HIV in at-risk urban areas was maintained by the interaction of geographic contiguity, compound risk (multiple risks with multiple people), and network structure.4,5 This hypothesis rested on data from diverse sources. We have since conducted a study of the personal geographic space, risk-taking, and network structure of two groups at different levels of HIV risk in communities in the inner city of Atlanta GA. We present here data that confirm some features of the hypothesis.
The Atlanta Metropolitan Area, defined by the 2010 US Census as a 20-county area, has a population of 4,228,492. The City of Atlanta (hereafter, Atlanta) houses less than 10% (420,003) of the metro area.6 We conducted this study in 10 Atlanta ZIP codes—5 in higher-risk areas (total population, 147,938) and 5 in lower-risk (total population, 197,195),7 between 2006 and 2011. The higher-risk ZIP codes have an area of 67 square miles; the lower-risk ZIP codes, 173 square miles. They share a border that is 16.7 miles in length. These groupings were selected based on their HIV reporting history at the start of the study; the higher-risk area accounted for 10 times more reported cases than did the lower-risk area (Fig. 1).
To construct a full network (that is, a sociogram that connects interviewed persons, as opposed to an egocentric design, that only identifies contacts to a respondent, but does not connect respondents), we used a chain-link design.8 This approach uses one of the contacts that a respondent names as the next link in a chain. We chose this design in preference to the major alternative—a snowball design wherein all contacts are interviewed, then all the contacts of contacts are interviewed—because of logistical and some statistical advantages.
We spent 6 months conducting ethnographic assessment of the neighborhoods in these areas and recruited 30 persons to act as “seeds” for the study—3 in each of the 5 ZIP Codes in each of the 2 areas. We recruited an equal number of men and women who qualified by being 18 years or older, being involved in HIV risk-taking, either through use of drugs or sexual activity, and who communicated a willingness to name and discuss their partners. Each seed was interviewed using a standard survey instrument, tested for HIV and sexually transmitted diseases, and information about their contacts was elicited. We sought demographic and risk information about all partners and interviewed as many as we could locate. In addition, we sought specific geographic information on a variety of sites that helped define the respondents' geographic range (home, if any; usually places for sleeping; centers of activity (meaning where the respondent spent a lot of time and could be usually found); sites at which risk activity took place; specific sites of contact with each partner; other frequented places). Sites outside Atlanta were not named commonly and were not included in the construction of geographic range. The latitude and longitude of each site named was obtained using either automated procedures or direct measurement using a global positioning system device at the site. In all, 3771 unique sites were identified and geocoded.
We asked each seed to nominate a contact as the next person in a chain. That person was interviewed, tested, and asked to name a third person in the chain. We maintained a running tally of persons named, deduplicating “on the fly” by matching persons with the same demographics and using the field team's knowledge of the clients for final confirmation. For the large number of contacts named but not interviewed, we used an algorithmic matching program based on contact characteristics. The final network from each seed was thus a connected component of 3 major respondents, all their interviewed contacts, and all their uninterviewed contacts. These 30 networks contained 927 interviewed persons, and a total population of 11,323 distinct individuals.
We defined compound risk as performing multiple different risky acts with multiple partners. We chose 6 variables as representative of such risks: having sex with 10 or more different partners of either gender in the past 6 months, having sex with 6 or more male partners in the past 6 months, any history of injection drug use, history of ever engaging in sex work, history of ever having had sex with an injecting drug user, history of anal sex in past 6 months. Compound risk was a binary variable: negative for zero or one of these 6 risk factors, positive for 2 or more risk factors. We assessed the frequency of personal and dyadic characteristics in the comparison of the high-burden with the lower-burden areas. These analyses were conducted using SAS, version 9.3.9
For each individual who was interviewed, we constructed a polygon of personal geographic range based on the coordinates of the sites named. The centroid of this polygon was used to calculate that individual's geographic locus for computation of geographic distance between individuals and for comparison of geographic distance with social distance, measured as the geodesic (shortest path) between two persons. For each of the 30 networks, the polygons of each member were overlaid on a map of Atlanta and the extent of overlap assessed. We used the extent of overlap to estimate geographic compactness of the network (for example, the size of the area that contained overlap of 50% of network members). These calculations and visualizations were conducted using ArcGIS.10
We measured the structural characteristics of the 30 networks, each of which was a connected component by design, using UCINET 6.11 The major differences among them would then center on the number of persons named in common by those interviewed and the types of connections. All ties were considered undirected, on the assumption that sexual contact, drug use, and acquaintanceship had to be mutual activities. Time of first and last contact was obtained, but the networks were temporally compressed for this analysis. We calculated the major network parameters, using several measures of degree, structure, and cohesion. In particular, we used point connectivity—defined as the number of nonoverlapping paths between persons, equivalent to the number of intervening persons who would need to be removed to “disconnect” 2 persons—as a measure of the extent of interconnectedness in a subgroup. We examined the distribution of the number of contacts named per person by calculating the power law fit (where a is the power law coefficient in the formula y = bx−a) using the method described by Newman.12
For this study, we use “dyad” to refer to any 2 people who are connected to each other by a pathway of any length. We combined network and geographic structure by examining the joint distribution of geodesic network distance (the minimum number of nodes separating each connected dyad) and geographic distance (the distance in kilometers separating each possible dyad, measured, as noted above, from the centroid of each person's polygon).4 The content of each cell of the geodesic-geographic matrix is the number of dyads whose members were at that specific social and geographic distance from each other (Supplemental Tables 1 and 2, http://links.lww.com/OLQ/A146). The matrix of social distance and geographic distance permitted simultaneous assessment of differences in compactness of networks in lower-risk and higher-risk areas. Because the higher-risk geographic area was greater than that of the lower-risk area, we compared the proportional distribution of geographic distances, summed over all geodesic distances to determine if the greater land mass contributed to differences in the joint geographic-geodesic distributions.
The prevalence of HIV in the lower-risk area was 12% and, in the higher-risk area, 17%. In general, there were few major differences between the higher-risk and lower-risk groups. Greater than 90% of study participants were African American, with a male-to-female ratio of 1.1 (Table 1). Approximately 70% were single, and about 40% had a high school diploma or GED. The unemployment proportion was similar in men and women (49–59%), but homelessness was considerably less in the lower-risk areas and among women (male: lower, higher: 10%, 27%; female: lower, higher: 6%, 21%). Over 90% of men in both areas had been incarcerated; among women, the proportion varied from 63% (lower risk) to 77% (higher risk).
Less than 30% of men and women in both areas classified their health as excellent (Table 2). The majority of both sexes and persons in both areas self-identified as heterosexual, but a greater proportion of women stated that they were bisexual, especially in the higher-risk area (15.8% vs 1.3%). A history of gonorrhea, syphilis, chlamydia, and (among women) trichomoniasis was common in both areas. The vast majority of participants smoked (88% to 95%). Crack use was about twice as great in the higher-risk areas and was similar in men and women. Heroin use, and drug injection were highest among men in the higher-risk area (22%), and anal sex in the past 6 months was highest among women in the higher-risk areas (12%).
Compound Risk Characteristics
Of the 6 components of compound risk (Table 3), 6 or more male sex partners (counting men and women together, because men who had sex with men were a small proportion of the total) was the most common (26.2%). Among the 797 participants for whom full information was available (398 in the lower-risk area; 397 in the higher-risk area), 10.9% exhibited 2 or more of the components of compound risk. There were 24 such persons in the lower-risk area (5.7%) compared with 73 persons in the higher-risk area (15.5%) (z score for difference of proportions = 4.6; exact P = 3.1 × 10−5).
The matrix of social distance (shortest number of steps between any two participants) and geographic distance (length in kilometers between the centroids of any 2 participants) for the lower-risk area was 10 × 21, and for the higher-risk area was 8 × 21. For the lower-risk area, 19.4% of dyads were within 1.0 km or less of each other; for the higher-risk area, 29.1% were within 1.0 km of each other. Comparing successively larger square subsets of each matrix, from a social distance of 1 (direct contacts) and a geographic distance of 1 or less, to a social distance of 8 or less and a geographic distance of 8 or less, at every point, the higher-risk areas demonstrates greater compactness (Fig. 2). The 1 × ≤ 1 cell in both the higher-risk and lower-risk areas both contain 0.8% of the dyads. The 8 × 8 square contains 66% of the dyads in the higher-risk area and 57% of the dyads in the lower-risk area. The proportional distributions of geographic distance demonstrated a ratio of 3:2 (higher to lower) for distances of 1.0 km or less, but the distributions were almost identical over the remainder of the distances (Fig. 3).
The amount of overlap of the geographic range of participants in higher and lower areas also differed substantially. On average, the area that contained overlap of 50% of lower-risk group members was 10.6 km; for higher-risk group members, 50% overlap occurred over 3.8 km. When the 15 groups in each area are ranked by the size of the overlap of 50% of participants, the difference in distributions is evident (Fig. 4) and demonstrates a markedly greater compactness within the higher-risk area.
The 30 networks varied in size from 111 to 512 nodes, with an average of 248.5 in the lower-risk area, and 288.3 in the higher-risk area. The screening set of network measures that we used to compare the higher and lower configurations yielded several small differences (Table 4). On average, there were more components per network in the higher-risk areas (7.27) compared with the lower-risk areas (4.33) but the proportion of persons in the largest component in higher and lower networks were almost identical (lower, 0.79; higher, 0.77). The degree mean and variance were virtually the same in both areas as was concurrency, transitivity, the average distance between nodes and the network diameters (the largest geodesic). Using the degree (number of contacts) of each node as the measure of centrality, the average degree was marginally larger in the lower-risk group (9.32 [lower] vs 8.73 [higher]). Average point connectivity (the number of disjoint paths between any 2 connected nodes) was slightly higher in the higher-risk area (0.98 vs. 0.91) (Table 4, legend).
As with other network measures, the distributions of numbers of contacts, calculated as a power law model and displayed as the log of the number of contacts against the log of the proportion of contacts, were similar (Fig. 5). The power law coefficients were 2.4 for the lower-risk area and 2.3 for the higher-risk area. We did not attempt to determine if the power law fit was the best modeling approach13,14 but used it as an heuristic for comparison of the 2 distributions.
The motivating premise for this study—that endemic HIV transmission in at-risk communities is maintained though the interaction of intense compound risk taking, geographic compactness, and a conducive network structure—is based on the idea that, within such a configuration, new partners are persons who are likely to be part of the same network, and therefore face a higher prevalence than would be present in a broader, less confined population. The hypothesis is supported in part by the data. We had postulated that differences in all 3 elements would be apparent in communities at differing risk for HIV transmission. In comparing a higher-risk area (with a 19% prevalence in this study) to a lower-risk community (with a 12% prevalence), we demonstrated that the former had a 3 times greater frequency of compound risk (15.5% vs 5.7% for the occurrence of 2 or more major risks in individuals), and evidence of substantially higher geographic compactness (greater overlap of personal geographic space and more intense joint social and geographic proximity). The social network structure in both groups was similar, however. Both higher-risk and lower-risk areas displayed evidence of highly connected and interactive groups, with large connected components that contained on average three fourths of the group members in each of the 30 networks examined, high levels of concurrency, and a long tail to the right in the distribution of numbers of contacts per person.
These data suggest an alternative view of the maintenance of HIV endemicity in at-risk communities than the one originally suggested. Though the data are not presented here in detail, most of the network parameters we calculated varied over a substantial range, but that variation was similar in the 2 risk areas. The networks, then, form an infrastructure for transmission. The amount of transmission depends on other factors—here, the intensity of risk and geographic compactness—that can promote or obstruct viral acquisition. A third major factor would be the effect of treatment programs on community viral load,15 but this study was conducted before major policy changes that recommended universal HIV treatment regardless of clinical status.16
The importance of networks in disease transmission is firmly established,17 though considerable work is currently devoted to understanding the specific relationship of network configuration and transmission.18,19 The importance of the interaction between risk taking and network configuration is also well established, especially to highlight the independent role that network structure, such as concurrency,20–23 plays in transmission dynamics. Geographic aspects of network relationships have received increasing attention in recent years.24 Techniques for visualizing networks in real space have been developed, and considerable theoretical and modeling endeavors have attempted to distinguish the effects of geographic proximity and network relationships. A number of empirical studies have explored the general relationship of geographic distance to social ties, and have examined the joint geographic-social effect in criminal activity, communications networks and economic activity (documentation of these observations is provided in supplemental references 30s–53s, http://links.lww.com/OLQ/A146).
For empirical studies of infectious disease, joint social and spatial analyses have received less attention. Heimer et al.25 mapped the location of 788 of 900 participants in a study of IDU and HIV infection in St. Petersburg, Russia, and found that the HIV positives (29.9%) were tightly clustered in 5% of the populated areas of the city. Network ties among participants were not reported. In an analysis of diarrheal illness in Ecuador, Bates et al.26 found that diarrheal risk was higher is less dispersed communities and lower in communities with lower social connectedness (lower degree). Giebultowicz and colleagues27 used kinship networks and the distance between persons in different neighborhoods to examine cholera distribution in Matlab, Bangladesh, over a 21-year period. Their result suggested that social ties had a less consistent relationship to cholera clustering than did proximity and (probably) unmeasured environmental variables. In a subsequent analysis,28 the same group using female kinship networks and the configuration of roads to demonstrate the clustering of diarrheal illness near roads, and the lesser effect of networks. Several studies have used geographic information to examine disease clustering (with the presumption of network relationships). In a single study directly relevant to this one, Hixson et al.29 demonstrated marked clustering (60%) of prevalent HIV cases in downtown Atlanta, with a prevalence within the cluster area more than 4 times greater than the prevalence outside it. Nearly half of the identified HIV providers in the Atlanta area were situated within the area of highest clustering, and their geoposition was used to determine presumed travel times. Like several previously cited, however, this study did not examine actual network connections.
There is, then, little available in the extant literature for comparison with our results. Given the sample of 30 networks, it is tempting to assume that these networks are representative of those that would be encountered in disadvantaged at-risk communities. It would be injudicious, however, to assume that our joint examination of risk, network attributes, and geographic configuration is generalizable. Several other limitations constrain conclusive statements. These data are cross-sectional comparisons, and it is well understood that behavior at the time of acquisition of HIV may not be reflected in current behavior. The assumption for a stronger conclusion would have to be that the transmission milieu reflects that of an earlier time. Another caveat for these results is that the temporal nature of contact relationships is not considered, and chronology may play a critical role in understand network contributions to transmission.30 Because the lower-risk geographic area was substantially larger than that of the higher-risk area (a ratio of approximately 3:1), some of the distance differences could be attributable to a population that was more “spread out.” The distribution of geographic distances in each area, summed over all social (geodesic) distances, suggests that a greater proportion of dyads were at 1 km or less from each other, but that distance between dyads was otherwise similar. It is likely that neighborhood aggregation had a greater effect than overall land mass. It is possible that seeds in the lower-risk areas had less risky behavior than seeds in the higher-risk area—a self-fulfilling prophecy. Though we made every effort to enroll similar people in both areas (and their demographics and behavioral characteristics were similar; data not shown), an intrinsic difference in seeds may still be a possibility. Such differences could potentially affect the results if respondents have systematic differences in willingness to communicate information about partners. A segment of our interview includes a postinterview assessment of accuracy and veracity and we detected no difference between the higher and lower prevalence areas (data not shown).
The alternative hypothesis that these data suggest should not be interpreted to mean that networks are not important. Rather, they might indicate that a certain “minimum” network is required to maintain endemicity, with, as noted, other factors that determine the prevalence set point. This concept lends itself well to modeling and simulation, since each of the factors invoked—risk configuration, geographic relationships, (and treatment)—can be modeled in the context of the empirical network observations. Further exploration of the interaction of the multiple factors that affect transmission is likely to provide greater insight into the dynamics.
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