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
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 freetext
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.
Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies. Population Health Metrics, 5 (1).
Revealing the burden of maternal mortality: a probabilistic model for determining pregnancy-related causes of death from verbal autopsies.