Scoping review : stature estimation equations in forensic anthropology and their applications to diverse populations

Abstract

In forensic anthropology, stature is one of four biological traits used in identification of individuals. The mathematical method is one of the more reliable methods to estimate stature. It encompasses displaying a correlation between a single or combination of bones with stature through regression equations. The major disadvantage is that these equations are highly population specific and cannot be used in Australia, which is a diverse and multicultural society. Thus, the aim of this project was to conduct a scoping review to systematically identify studies (from the last 50 years) exploring equations for stature estimation that are non-population specific. Scopus, Web of Science and Medline on Ovid were searched using the following key words: "stature estimation" OR "body height" OR "human identification" AND "long bones" or bones AND "regression". This search produced 664 studies across selected databases and studies were selected in a structured approach. The studies that had addressed the inclusion criteria were then reviewed in full. Relevant information and regression equations that may be applied to estimate stature for diverse populations were extracted. Majority of the studies investigating stature were population specific, with studies conducted primarily in the Asia region. An increase in the use of computed tomography (CT) to obtain bone measurement was evident in many recent studies to estimate stature. Seven studies were identified with potential to applied to the diverse Australian population. Five from the Southern region of Africa, one based in Nigeria and a generic equation developed in Canada. These studies investigated equations using skeletal material that could be applied to diverse groups and were tested on a mixed population. The effectiveness of these identified equations need to be tested on the current Australian population.

Date of Award	2018
Original language	English