Tyler McCormick sent along this paper, “Probabilistic Cause-of-death Assignment using Verbal Autopsies,” coauthored with Zehang Li, Clara Calvert, Amelia Crampin, Kathleen Kahn, and Samuel Clark:

In areas without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This paper develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods.

As I’ve been saying a lot recently, measurement is a central and underrated aspect of statistics, so I’m always happy to see serious research on measurement-error models. I hope this project is directly useful and also stimulates further work in this area.