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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.

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|November 12, 2024
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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.

Keywords:
Crude frequenciesGeneralized linear (mixed) modelsSimulationsSkeletal preservationTrauma prevalence

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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.