Maternal major depression disorder misclassification errors: Remedies for valid individual- and population-level inference.

ABSTRACT

Individual and population level inference about risk and burden of MDD, particularly maternal MDD, is often made using case-finding tools that are imperfect and prone to misclassification error (i.e. false positives and negatives). These errors or biases are rarely accounted for and lead to inappropriate clinical decisions, inefficient allocation of scarce resources, and poor planning of maternal MDD prevention and treatment interventions. The argument that the use of existing maternal MDD case-finding instruments results in misclassification errors is not new; in fact, it has been argued for decades, but by and large its implications and particularly how to correct for these errors for valid inference is unexplored. Correction of the estimates of maternal MDD prevalence, case-finding tool sensitivity and specificity is possible and should be done to inform valid individual and population-level inferences.