The Tariff Method is a simple additive approach based on identifying items in a VA interview that are indicative of particular diseases. It is based on the premise that individual items or signs/symptoms should be more prominently associated with certain causes (the "signal") compared with others (the "noise"). This simple approach performs as well as or better than PCVA for adult causes in assigning an underlying cause of death, though PCVA performs better in this comparison for child deaths. At the level of particular causes, Tariff has higher chance-corrected concordances than PCVA for 14/34 adult and 8/21 child causes. Results for neonatal deaths are not comparable due to differences in cause lists. For estimating CSMFs, Tariff performs better than PCVA for adult and child deaths in all comparisons with and without household recall of health care experience. In all comparable cases, Tariff yields higher median CSMF accuracy than PCVA. Overall, at the individual and the CSMF level, Tariff in general offers a competitive alternative to PCVA. Performance for assigning neonatal causes of death, however, is worse than for PCVA.
The tariffs for each cause-item pair have already been established using Stata code, which will be available online. Using this pre-existing tariff matrix, the Tariff Method requires only multiplication and addition to make cause of death assignments for each individual death in a given dataset. Though we processed VA response data to develop our method, users need not conduct additional processing to use Tariff since our processing steps can be integrated into the code that makes cause of death assignments. The absence of a statistical model or complex computational algorithm means that the steps involved in assigning cause of death to a particular death can be completed in a spreadsheet and are readily available for user scrutiny. Further, the tariff matrix and algorithm can be implemented on a simple device such as a cell phone - the Open Data Kit research team at the University of Washington has already implemented the tariff algorithm on an Android cell phone using their Free/Libre Open-Source Survey Platform. In other words, tariff-based cause assignments can be made immediately after data collection in the field.
One of the key strengths of Tariff is its flexibility. Each item's tariff for a cause is computed independently from all other items. Consequently, any instrument's verbal autopsy items that can be mapped to one of the items in the PHMRC dataset can be evaluated using Tariff. Other methods, such as Random Forest and Simplified Symptom Pattern, require the testing data to have the same item set as the data on which the model was trained. This is an important asset of Tariff because it allows users to implement the method without having to recalculate tariffs or revise the algorithm. It can essentially be used as is for any verbal autopsy instrument with overlapping items with the PHMRC instrument.
Tariff does not take into account the interdependencies of signs and symptoms conditional on particular causes. It does not take into account the complex time sequence captured in open narratives, which are often used by physicians. How can such a simple algorithm be more effective than physicians? The answer may lie in the key attributes of Tariff that distinguish it from other methods: identification of items that are unusually important for different causes through computation of the tariff and the additive rather than multiplicative nature of the tariff score. The tariffs focus attention on the specific subset of items that are most strongly related to a given cause. The additive approach may make Tariff more robust to measurement error either in the train or test datasets.
Because of its simplicity, we plan to make available several different platforms on which to apply Tariff. Programs in R, Stata, and Python will be available for assigning a cause for a given death or set of deaths, as well as a version of Tariff in Excel for users without training in statistics packages. Tariff will also be available in the Open Data Kit for use on the Android operating system for cell phones and tablets. We hope these tools will lead to widespread testing and application of Tariff. The full sign/symptom-cause tariff matrix will also be available for user inspection and application to other verbal autopsy diagnostic methods such as Random Forest and Simplified Symptom Pattern, which rely on tariffs to identify meaningful signs and symptoms. The tariffs can also be used to refine further verbal autopsy instruments, possibly in reducing the number of survey items, since they show which specific signs/symptoms should be included for accurately predicting certain causes of death. For example, one strategy for item reduction would be to drop items that have low tariffs for all causes and then assess the change in CSMF accuracy or chance-corrected concordance when cause assignment is undertaken with the restricted item set.
Given that PCVA can be costly and time consuming, it would seem that Tariff provides an attractive alternative. Compared to the current version of InterVA , Tariff performs markedly better. We believe that users interested in rapid, low-cost, easy-to-understand VA methods should consider Tariff. As indicated by analysis of CSMF accuracy and true versus estimated CSMF regressions, there are certain cases where Tariff may overestimate or underestimate CSMFs for particular causes. It will be important for users of Tariff to understand these limitations, particularly for the purposes of using Tariff to better inform public health decision-making. Future research may yield new techniques to more accurately determine CSMFs based on verbal autopsy through back calculation. Tariff is also attractive to those who wish to examine the exact computation by which a verbal autopsy algorithm makes a cause of death assignment. In the future, as more gold standard deaths are collected to augment existing causes in the PHMRC dataset, or for new causes, it will be straightforward to revise existing tariffs or report tariffs for new causes. This step is particularly easy compared to other computer-automated methods, for which expansion with more causes requires revision of the algorithm itself.