To identify recent studies using the confidante method, we searched MEDLINE with the terms “abortion incidence” OR "abortion safety" AND "measure*", for journal articles, observational studies, reviews or systematic reviews published in any language before June 25, 2020. Out of 40 published studies, we identified seven applications of the confidante method in: Côte d’Ivoire, Ethiopia, Ghana, the island of Java-in Indonesia, Nigeria, Uganda, and Rajasthan state in India.9–12 Five of the seven studies were fielded on the Performance Monitoring for Action (PMA) survey platform.13 All surveys were cross-sectional and fielded in 2018. The sampling strategies for each survey were designed to produce nationally representative samples, except for Rajasthan and Java, which were designed to be representative of those sub-national regions. The Technical Appendix provides additional details about the underlying studies, including their sampling strategies, final sample sizes, measures, and other analytic information not presented in the main body of this paper.
In all applications of this method, respondents are first asked to think of all the women they know who fit the definition of a confidante. While the exact definitions varied across the seven confidante method applications included in this paper (see Technical Appendix, Table B), they all describe close social ties with whom the respondent shares private information. A key feature of the method is that the confidante definition explicitly states that this relationship must be reciprocal (i.e., confidantes also share private information with the respondent.) In four of the recent applications, respondents could report abortion information on up to three confidantes.10–12 In the other three applications, respondents were asked to only report on two confidantes.9 Given the small proportions of women who were able to identify three or more confidantes, we limit our analytic sample to the first and second reported confidantes to ensure comparability.
The core questions for this analysis included the total number of confidantes reported, whether the respondent and confidantes had obtained induced abortions, the month and/or year of the respondent and confidante’s most recent abortion, the degree of certainty respondents had about the induced abortions reported for confidantes (certain and less certain), and whether the respondent had told any of the confidantes about the respondent’s induced abortion experiences (see Technical Appendix for more details).
We estimate one-year induced abortion incidence rates for confidantes and respondents in each country. For all rates, the numerator includes all abortions that occurred in a specified 12-month time frame. The denominator is the number of respondents or confidantes in the analytic sample. To be included in the confidante rate, respondents had to indicate that they were “certain” that the abortion occurred. We then multiply each rate by 1,000 to get the rate per 1,000 women of reproductive age (15-49 years) in the corresponding population. Next, we examine the existence of biases across the seven confidante datasets using the confidante method assumptions described in Giorgio et al. When possible, we also attempt to adjust for selection bias, reporting bias, and transmission bias in a standardized way across the seven samples.
Selection Bias: One of the most important assumptions of the confidante method is that respondents select confidantes with homophily, which is the principle that a contact between similar people occurs at a higher rate than among dissimilar people.14 To determine this, we compare the distributions of available sociodemographic characteristics between respondents and their confidantes. In cases where violations of the homophily assumption were identified, confidante incidence estimates were weighted using post stratification weights created using multiple logistic regression to make the sample representative of the population sampled. (Respondent abortion incidence estimates were weighted using the sample weights generated by PMA or the original study team.) Due to variability in sociodemographic variables collected across contexts and a lack of appropriate auxiliary variables, we were unable to use multiple imputation to construct post stratification weights for all contexts in a standardized manner. The Technical Appendix outlines the procedure applied in each context.
We also assess the existence of barrier effects, which would result in study samples missing an important parts of the population.15,16 To do this, we used Poisson regression to estimate unadjusted prevalence ratios (uPRs) for the relationship between key respondent sociodemographic characteristics and reporting any (versus no) confidantes.
Reporting Bias: Given the risk that more recent abortion reporting may be more prone to backward telescoping,17 thereby influencing the validity of the annualized estimates, we compare induced abortion estimates for 2017 (where data were collected for a full year in each context) with annualized estimates for 2018 (where data were collected for a few months in the year). We also compare the 2017 abortion incidence estimates of respondents to their confidantes to check for recall bias.
Transmission Bias: Previous research notes the importance of accounting for the visibility of abortions when using social-network based methods to estimate abortion incidence.4,18 We apply three methods that attempt to adjust for underreporting due to transmission bias. In one scenario, we included all less certain abortions, regardless of the availability of additional information. In the second scenario, we apply the method detailed by Bell et al. (2020) and include less certain abortions where respondents were able to provide additional information about the abortion (where this data was available) in incidence estimates.9 In the final scenario, we estimate the proportion of respondents self-reporting abortions who shared their experiences with the reported confidantes. Using this information, we apply a correction factor to the base incidence estimates, which is estimated as the inverse of the proportion of respondents who self-reported abortions and had informed any of their confidantes. (See Technical Appendix for a more detailed explanation for the three adjustment methods).
Finally, we conduct a risk of bias assessment on previous publications from each context to examine which assumptions of the confidante method had been evaluated as part of the analysis and the degree of fulfillment or violation of these assumptions. We also compare all confidante adjusted incidence estimates to previously published confidante method estimates from these data to understand how differences in analytic decisions affect resulting incidence rates and other available incidence estimates from the context to understand the relative performance of this method.