A new approach to measuring partnership concurrency and its association with HIV risk in couples.

Empirical estimates of the association between concurrent partnerships (CP) and HIV risk are affected by non-sampling errors in survey data on CPs, e.g., because respondents misreport the extent of their CPs. We propose a new approach to measuring CPs in couples, which permits assessing how respondent errors affect estimates of the association between CPs and HIV risk. Each couple member is asked (1) to report whether s/he has engaged in CPs and (2) to assess whether his/her partner has engaged in CPs, since their couple started. Cross-tabulating these data yields multiple classifications (with varying combinations of sensitivity/specificity) of the CPs of each couple member. We then measure the association between CPs and HIV outcomes according to each classification. The resulting range of estimates is an indicator of the uncertainty associated with respondent errors. We tested this approach using data on 520 matched couples drawn from the Likoma Network Study. Results suggest that existing tests of the concurrency hypothesis are affected by significant uncertainty.