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Sexual Mixing in Shanghai: Are Heterosexual Contact Patterns Compatible With an HIV/AIDS Epidemic?

  • Published:
Demography

An Erratum to this article was published on 21 May 2015

Abstract

China’s HIV prevalence is low, mainly concentrated among female sex workers (FSWs), their clients, men who have sex with men, and the stable partners of members of these high-risk groups. We evaluate the contribution to the spread of HIV of China’s regime of heterosexual relations, of the structure of heterosexual networks, and of the attributes of key population groups with simulations driven by data from a cross-sectional survey of egocentric sexual networks of the general population of Shanghai and from a concurrent respondent-driven sample of FSWs. We find that the heterosexual network generated by our empirically calibrated simulations has low levels of partner change, strong constraints on partner selection by age and education, and a very small connected core, mainly comprising FSWs and their clients and characterized by a fragile transmission structure. This network has a small HIV epidemic potential but is compatible with the transmission of bacterial sexually transmitted infections (STIs), such as syphilis, which are less susceptible to structural breaks in transmission of infection. Our results suggest that policies that force commercial sex underground could have an adverse effect on the spread of HIV and other STIs.

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Notes

  1. Nonmarital partners could include previous marital partners of currently remarried respondents or of divorced but currently unmarried respondents.

  2. The rationale for limiting the analyses to first marriages is because in cross-sectional data on prevailing marriages gathered from multiple birth cohorts, first marriages can lessen biases due to variation in marriage timing and divorce rates, which may affect the degree of resemblance between spouses in a cohort (Kalmijn 1991; 1998; Mare 1991; Qian 1998; Qian and Preston 1993; Raymo and Xie 2000). We do not expect this exclusion to be problematic for our analyses. Because remarriage and divorce are rare in this setting, there is no current evidence of differential risk for STI exposure by marital status. Inclusion of this additional stratifying feature in our models and measures would have created estimation issues as the cell sizes are too small to provide stable mixing estimates. However, because divorce and remarriage are likely to become increasingly prevalent in China, this is an area deserving attention in future work.

  3. The advantages of this randomized draw procedure over other graph simulation approaches—particularly exponential random graph models (ERGM) (e.g., Goodreau et al. 2009)—is mainly computational efficiency. We can generate large networks consistent with our population in mere seconds, which we can then evaluate for connectivity features. Because the information we have is purely at the node and dyad level—including no other edge-dependent features—the resulting models are effectively identical to a dyad-independent ERGM similarly based on node mixing and degree. The addition of the assortativity features creates some level of dependence, but in practice, these are largely determined by birth cohort/education differences in degree.

  4. We assessed the extent to which variation in the setting of FSW degree affected the sizes of the maximum component and the bicomponent, but their distributions did not vary significantly. The mean of the maximum component varied from 15.7 % to 16.7 % of all nodes with each of the three settings of FSW degree, while the mean of the bicomponent varied from 10.7 % to 9.8 % of the largest component; this suggests that a key feature of these simulated networks is the base frequency distribution of the degree of vertices, not variation in the FSW degree settings. To push this assumption of the model, we ran additional simulations allowing the tail of the FSW degree distribution to reach as high as 250; we found no difference in the epidemic potential measures.

  5. This image is generated by a single run with values chosen to represent the middle of the value ranges used in the complete simulation experiment. We choose a maximum FSW degree of 60 with M = 27,500 edges.

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Acknowledgments

Collection of the survey data analyzed here was undertaken by the first author in collaboration with Ersheng Gao and Xiaowen Tu at Fudan University School of Public Health, and Anan Shen at the Shanghai Academy of Social Sciences. The Shanghai Sexual Network Survey (SSNS) was funded by NICHD/NIDA Grant R21HD047521, supplemented by two smaller grants from NICHD (Merli, PI). The Shanghai Women's Health Survey (SWHS) was funded by a Ford Foundation grant to Ersheng Gao. The SSNS sample was designed in consultation with William Kalsbeek. Data analyses were funded in part by NICHD Grants R01 HD068523 (Merli, PI) and R01 HD075712 (Moody, PI). We thank the seminar participants of the Center for Studies in Demography and Ecology at the University of Washington and of the Population Research Center at the University of Texas at Austin, Ashton Verdery, and two anonymous reviewers for constructive comments.

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Authors and Affiliations

  1. Duke Population Research Institute, Sanford School of Public Policy, Department of Sociology and Duke Global Health Institute, Duke University, Box 90312, Durham, NC, 27708, USA

    M. Giovanna Merli

  2. Duke Population Research Institute, Department of Sociology, Duke University, Box 90088, Durham, NC, 27708, USA

    James Moody

  3. King Abdulaziz University, Jeddah, Saudi Arabia

    James Moody

  4. RAND Corporation, 1776 Main Street, Santa Monica, CA, 90401, USA

    Joshua Mendelsohn

  5. REACH Lab, Department of Sociology, University of Nebraska–Lincoln, 209 Benton Hall, Lincoln, NE, 68588-0623, USA

    Robin Gauthier

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Appendix

Appendix

Sampling Scheme, Adjustment For Nonresponse and Development of Sampling Weights

Sampling Scheme

The samples of Shanghai registered residents and migrants were selected as random subsamples of Shanghai registered residents and migrants from a stratified multistage clusters sample screened by the Shanghai Statistical Bureau for the 2005 3 % intercensal survey of the Shanghai population.

The design of the 3 % sample produced a stratified two-stage sample of persons, where 963 neighborhood committees (NCs) consisting of 2,000–5,000 persons were selected as primary sampling units (PSUs), with probabilities proportional to their estimated number of residents and migrants. Explicit stratification of NCs was done by the 19 districts/counties (18 urban districts and 1 rural county). NC selection in the first stage was also implicitly stratified within each district/county by the estimated ratio of resident population to migrant population in sorting the NCs for probability proportional to size (PPS) systematic selection within each district/county. Allocation of the 963 NCs among districts/county was proportionate to the population of the stratified subsample of NCs. In the second sampling stage, all persons (and households) were included within 2,151 small groups (SGs, approximately 100 persons each) that were chosen within each sample NC by unstratified simple random sampling.

The subsamples of residents and migrants for the SSNS was selected from the 3 % sample to yield a stratified four-stage sample of Shanghai residents and migrants aged 18–49. In Stage 1, for both subsamples, 50 NCs were first subsampled from the 963 NCs selected for the 3 % sample. This was accomplished by simple random sampling without replacement within each of the following three groups of the 19 districts/counties used for the 3 % sample: central city, inner suburbs, and outer suburbs. Allocation in the stratified subsample of NCs was proportionate. In Stage 2, exactly two of the two or three SGs that were selected for the second stage of the 3 % sample were retained for the SSNS subsample, yielding 100 SGs subsampled. Separate subsamples of 18- to 49-year-old residents and migrants were then subsampled in the two remaining subsampling stages. In Stage 3, for the resident subsample, 12 registered households were recruited within each selected SG using a currently updated list of household addresses in the SG as the sampling frame. In Stage 4, one 18- to 49-year-old household resident was randomly chosen from among those living in each participating household using a conventional Kish table. For the migrant subsample, a similar procedure was used with five migrant households recruited per SG using a currently updated list of household addresses with at least one migrant present.

Adjustment for Nonresponse

Among the 1,200 Shanghai registered residents and 500 migrants identified in the samples, participation rates were 56 % for Shanghai registered residents and 61 % for migrants; 17.7 % of Shanghai registered residents and 17.8 % of migrants refused to be interviewed, 14 % and 12 % did not participate because of failure to reach them, and 3.1 % and 3 % did not participate for other reasons. No reason was provided for nonparticipation for the remaining 9.7 % and 5.2 % of the samples. To prevent unit nonresponse from affecting the size of the samples of Shanghai residents and migrants, field substitution (Chapman 1983) was used to compensate for unit nonresponse. In the third sampling stage, within each SG, assigned and reserved samples of potential respondent households were selected according to random procedures. Respondents drawn from the assigned samples who did not participate in the survey were replaced with respondents drawn from reserve samples. Because substitution can easily be abused and the statistical integrity of the final sample can be compromised, we developed and rigorously enforced strict rules for replacing assigned addresses (for example, establishing when to call, what to say and do, and how many attempts to make before substitution). These were emphasized to both interviewers and supervisors at field training. A comparison of basic demographic characteristics of initial nonrespondents with those of respondents drawn from reserve samples and of participating respondents initially assigned for recruitment revealed no significant difference with respect to age and gender. This adjustment procedure yielded a total sample size adjusted for nonresponse of 1,192 Shanghai registered residents and 496 migrants.

Construction of Sampling Weights

Sampling weights were developed in two stages. The first stage of weighting consisted of computing the overall selection probability for each respondent in either the resident or migrant sample as the product of the stage-specific selection probabilities computed for each sampling stage (NC, SG, household, and household member). The individual sampling weight—w respondent —was computed as the inverse of the overall probability of selection. The second stage of weighting involved a further adjustment to this weight to make the resulting weighted estimates from the sample conform to known population values by age and sex for registered residents and migrants in the 2005 3 % intercensal survey. This adjustment was done for the samples of registered residents and migrants as follows: (a) Determine the unweighted sample frequency for each cell n cell defined by the cross-classified calibration variables (age and sex); (b) Determine the population count of 18- to 49-year-olds in the 3 % intercensal survey for each cell Ncell defined in the previous step; (c) Compute the calibration adjustment for all respondents in a cell as

$$ Adjustmen{t}_{cell}=\frac{N_{cell}}{Sum\_ of\_{W}_{respondent}\_ over\_ all\_{n}_{cell}\_ respondents}; $$

and (d) Compute the final calibrated weight for any respondent in a cell as

$$ {W}_{respondent, calibrated, cell}=\left({W}_{respondent, cell}\right)\left( Adjustmen{t}_{cell}\right). $$

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Merli, M.G., Moody, J., Mendelsohn, J. et al. Sexual Mixing in Shanghai: Are Heterosexual Contact Patterns Compatible With an HIV/AIDS Epidemic?. Demography 52, 919–942 (2015). https://doi.org/10.1007/s13524-015-0383-4

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