Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies

Abstract

Background: Substantial reductions in maternal mortality are called for in Millennium Development Goal 5 (MDG-5), thus assuming that maternal mortality is measurable. A key difficulty is attributing causes of death for the many women who die unaided in developing countries. Verbal autopsy (VA) can elicit circumstances of death, but data need to be interpreted reliably and consistently to serve as global indicators. Recent developments in probabilistic modelling of VA interpretation are adapted and assessed here for the specific circumstances of pregnancy-related death.

Methods: A preliminary version of the InterVA-M probabilistic VA interpretation model was developed and refined with adult female VA data from several sources, and then assessed against 258 additional VA interviews from Burkina Faso. Likely causes of death produced by the model were compared with causes previously determined by local physicians. Distinction was made between free-text and closed-question data in the VA interviews, to assess the added value of free-text material on the model's output.

Results: Following rationalisation between the model and physician interpretations, cause-specific mortality fractions were broadly similar. Case-by-case agreement between the model and any of the reviewing physicians reached approximately 60%, rising to approximately 80% when cases with a discrepancy were reviewed by an additional physician. Cardiovascular disease and malaria showed the largest differences between the methods, and the attribution of infections related to pregnancy also varied. The model estimated 30% of deaths to be pregnancy-related, of which half were due to direct causes. Data derived from free-text made no appreciable difference.

Conclusion: InterVA-M represents a potentially valuable new tool for measuring maternal mortality in an efficient, consistent and standardised way. Further development, refinement and validation are planned. It could become a routine tool in research and service settings where levels and changes in pregnancy-related deaths need to be measured, for example in assessing progress towards MDG-5.

Figure 1

Representation of the burden of each of the major cause of death categories derived by local physician review and InterVA-M interpretation of VA data for 258 adult female deaths in Nouna, Burkina Faso. The two central columns represent groups of causes to facilitate comparison.

Figure 2

Cause-specific mortality fractions (CSMF) for 258 female deaths (15–49 years) from Burkina Faso according to different interpretations of verbal autopsy data.