The Dirty War Index: Statistical Issues, Feasibility, and Interpretation

Perspective I n this issue of PLoS Medicine, Madelyn Hsiao-Rei Hicks and Michael Spagat propose a new quantitative tool [1] in the emerging field of human security sciences [2]. Quantitative data about people's insecurity can lead to objective knowledge about many of the contexts of violence in the world today. Thus developing quantitative tools for use in this domain is important. Hicks and Spagat's tool is called the Dirty War Index (DWI), which " systematically identifies rates of particularly undesirable or prohibited, i.e., " dirty, " war outcomes inflicted on populations during armed conflict " [1]. After choosing a relevant public health outcome, a DWI is calculated as the ratio of " the number of dirty cases " of an outcome to the " total number of cases. " There are many moral and legal questions regarding the division of acts of violence in armed conflict into those that are " dirty " and those—presumably—that are not " dirty. " I am not addressing these questions here. Instead, I will discuss some statistical issues that may arise in calculating a DWI, and the feasibility and interpretation of DWIs. Key statistical issues that may arise in the calculation and interpretation of a DWI are: (1) selection bias; (2) missing data; and (3) censoring (i.e., the value of an observation is only partially known). Selection bias. Many samples that are studied in examining the health effects of conflict on civilian populations are obtained from secondary sources such as media reports or hospital records. But these secondary sources almost always use different selection criteria compared to what would be used to derive a sample in a primary study focused on health effects of conflict. The use of secondary sources inevitably leads to selection bias: the health effects of conflict upon civilians in the sample are not representative of health effects due to conflict in the civilian population as a whole. If a biased sample is used to calculate a DWI, then the estimate will be inaccurate. Hicks and Spagat acknowledge that " bias can affect DWI values. " However, they argue that since a DWI is a ratio (rather than an absolute number), " DWIs are relatively less affected by under-or over-counting than absolute numbers. " They give the example of rape, and argue that " if a population generally under-reports war-related rape by 40%, this does not bias comparing rates between different …