Delayed decompositional changes in indoor settings among Tibetan monastic communities in India: A case report

Abstract

Within the fields of forensic anthropology and forensic medicine in general, the ability to provide accurate estimates of time since death that minimize error, maximize precision, and consider a multitude of variables is a critical component of all forensic and medicolegal investigations that focus on the identification of human remains and the circumstances surrounding death. In comparison with many forensic cases where date of death is typically unknown, we begin with a known postmortem interval and progressively assess, through external testing measures (temperature, skin elasticity and skin color changes) followed by photography and videography, decompositional changes over time. This work seeks to provide well-described cases with documented biological parameters to propose hypotheses for future research. Field team members observe and record any decompositional changes associated with the states of algor, rigor, and livor mortis, as well as progressive changes in skin color and tissue integrity in an environment where variability in climate and ecological factors may vary from case to case. The two cases were followed for 31 (postmortem interval [PMI] 38) and 19 (PMI 27) days in humid subtropical and tropical monsoon Köppen-Geiger classified climatic zones, respectively, where delayed decompositional and putrefactive changes observed are at odds with the anticipated rate and expected suite of biological changes occurring within an indoor permeable setting following clinical death. Within this taphonomic context, we also address the value of describing and documenting postmortem intervals in a closed subpopulation from which some individuals are recognized for exhibiting attenuated decompositional changes. This study emphasizes the importance of addressing understudied and underrepresented regions, climates, and cultural contexts for furthering research into the factors that may contribute to the understanding of the early stages of the PMI period and improve the precision of PMI estimates more broadly.