{Reference Type}: Journal Article
{Title}: The confidante method to measure abortion: implementing a standardized comparative analysis approach across seven contexts.
{Author}: Owolabi OO;Giorgio M;Leong E;Sully E;
{Journal}: Popul Health Metr
{Volume}: 21
{Issue}: 1
{Year}: 2023 07 25
{Factor}: 2.936
{DOI}: 10.1186/s12963-023-00310-0
{Abstract}: Obtaining representative abortion incidence estimates is challenging in restrictive contexts. While the confidante method has been increasingly used to collect this data in such settings, there are several biases commonly associated with this method. Further, there are significant variations in how researchers have implemented the method and assessed/adjusted for potential biases, limiting the comparability and interpretation of existing estimates. This study presents a standardized approach to analyzing confidante method data, generates comparable abortion incidence estimates from previously published studies and recommends standards for reporting bias assessments and adjustments for future confidante method studies.
We used data from previous applications of the confidante method in Côte d'Ivoire, Ethiopia, Ghana, Java (Indonesia), Nigeria, Uganda, and Rajasthan (India). We estimated one-year induced abortion incidence rates for confidantes in each context, attempting to adjust for selection, reporting and transmission bias in a standardized manner.
In each setting, majority of the foundational confidante method assumptions were violated. Adjusting for transmission bias using self-reported abortions consistently yielded the highest incidence estimates compared with other published approaches. Differences in analytic decisions and bias assessments resulted in the incidence estimates from our standardized analysis varying widely from originally published rates.
We recommend that future studies clearly state which biases were assessed, if associated assumptions were violated, and how violations were adjusted for. This will improve the utility of confidante method estimates for national-level decision making and as inputs for global or regional model-based estimates of abortion.