Counting unreported abortions: A binomial-thinned zero-inflated Poisson model

Abstract

Background: Self-reported counts of intentional abortions in demographic surveys are significantly lower than the actual counts. To estimate the extent of misreporting, previous research has required either a gold standard or a validation sample. However, in most cases, a gold standard or a validation sample is not available. Objective: Our main intention here is to show that a researcher has an alternative tool to estimate the extent of underreporting in a given dataset, particularly when neither a valid gold standard nor a validation sample is available. Methods: We adopt a binomial-thinned zero-inflated Poisson model and apply it to a sample dataset, the National Survey of Family Growth (NSFG), for which an alternative estimate of the average reporting rate (38%) is available. We show how this model could be used to estimate the reporting probabilities of intentional abortions by each individual in addition to the overall average reporting rate. Results: Our model estimates the average reporting rate in the NSFG during 2006‒2013 as 35.3% (SE 8.2%). Individual reporting probabilities vary significantly. Conclusions: Our estimate of the average reporting rate of the dataset used is qualitatively and statistically similar to the available alternative estimate.