Misclassification in defining and diagnosing microcephaly

Abstract

Background: Several health agencies define microcephaly for surveillance purposes using a single criterion, a percentile or Z-score cut-off for newborn head circumference. This definition, however, conflicts with the reported prevalence of microcephaly even in populations with endemic Zika virus.

Objective: We explored possible reasons for this conflict, hypothesising that the definition of microcephaly used in some studies may be incompletely described, lacking the additional clinical criteria that clinicians use to make a formal diagnosis. We also explored the potential for misclassification that can result from differences in these definitions, especially when applying a percentile cut-off definition in the presence of the much lower observed prevalence estimates that we believe to be valid.

Methods: We conducted simulations under a theoretical bimodal distribution of head circumference. For different definitions of microcephaly, we calculated the sensitivity and specificity using varying cut-offs of head circumference. We then calculated and plotted the positive predictive value for each of these definitions by prevalence of microcephaly.

Results: Simple simulations suggest that if the true prevalence of microcephaly is approximately what is reported in peer-reviewed literature, then relying on cut-off-based definitions may lead to very poor positive predictive value under realistic conditions.

Conclusions: While a simple head circumference criterion may be used in practice as a screening or surveillance tool, the definition lacks clarification as to what constitutes true pathological microcephaly and may lead to confusion about the true prevalence of microcephaly in Zika-endemic areas, as well as bias in aetiologic studies.

Keywords: Zika virus; epidemiology; microcephaly; misclassification.

Figure 1.. Example of a bimodal distribution with superimposed normal curves.

The histogram is the bimodal distribution with 20% at mean −4, SD 1 and 80% at mean 0, SD 1. The two curves are both normal curves with mean (SD) as follows: dashed line, −4 (1); solid line, 0 (1).

Figure 2.. Positive predictive value and prevalence estimates with varying sensitivity (Se) and specificity (Sp).

Note that the x-axis on all panels is shown in log-scale. Scenarios in each panel correspond to rows labeled with the same letters in Table 1.