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Scanning Skeletal Remains for Bone Mineral Density in Forensic Contexts
Published on: January 29, 2018
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Estimating trauma prevalence from incomplete human skeletal remains.
Judith Beier1,2, Matteo Santon3, Hannes Rathmann4,5
1DFG Center for Advanced Studies 'Words, Bones, Genes, Tools', University of Tübingen, 72070, Tübingen, Germany. judith.beier@uni-tuebingen.de.
Scientific Reports
|November 12, 2024
Summary
Estimating trauma prevalence from incomplete human skeletal remains is challenging. Generalized linear models (GLMs) offer more precise trauma estimates than traditional frequency methods, especially with missing skeletal data.
Area of Science:
- Bioarchaeology
- Forensic Anthropology
- Paleopathology
Background:
- Traumatic lesions on human skeletal remains are crucial for reconstructing past events and comparing trauma prevalence.
- Skeletal incompleteness introduces bias in trauma prevalence estimates, often addressed by excluding incomplete specimens.
- The impact of excluding incomplete remains on trauma prevalence estimates is not well understood.
Purpose of the Study:
- To compare the performance of the conventional frequency approach with generalized linear models (GLMs) for estimating trauma prevalence.
- To evaluate how different levels of skeletal incompleteness affect trauma prevalence estimates derived from these two methods.
Main Methods:
- A simulation framework was developed using empirical data from forensic, clinical, and archaeological contexts.
- Trauma prevalence was estimated using a conventional frequency approach (≥75% completeness) and GLMs incorporating completeness as a covariate.
- Estimates were compared against known trauma prevalence in simulated cranial samples with varying degrees of missing data.
Main Results:
- GLM-based estimates demonstrated superior precision compared to frequency-based estimates across all levels of skeletal incompleteness and sample sizes.
- Frequency-based estimates showed increasing inaccuracies in relative patterns between samples and occasional failures to produce estimates with greater incompleteness, particularly in smaller samples.
- GLMs provided more reliable trauma prevalence estimates, even with significant missing skeletal data.
Conclusions:
- Generalized linear models (GLMs) are recommended over traditional frequency methods for analyzing trauma prevalence in skeletal samples with missing data.
- While GLMs improve reliability, neither method is fully accurate for extensively incomplete skeletal samples.
- Future research should focus on developing robust methods for trauma analysis in highly fragmented skeletal assemblages.
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