Quantifying the Problem
Past studies of the postmortem interval show a lack of substantial data.
Investigators often use human substitutes such as pigs or mice. Previous studies have demonstrated that animal models are only of limited use as a proxy for human decomposition.
In the studies that used humans as the study subjects, the sample sizes range from 3-462 individuals. The study with N=462 examined vitreous humor, which is only useful for PMI determinations in a relatively short period around the time of death. The next largest sample size in a review of recent studies examined was 140 individuals. This study examined retrospective case studies from indoor settings. The median sample size among these human studies is 52 and the mean is 86 individuals.
Given the large number of variables known to impact the rate of decomposition, these sample sizes are insufficient for developing accurate models to determine PMI in individual cases. You cannot make a valid conclusion without more substantial data, which is why geoFOR has been created. This application allows you to help fill this critical gap and gather data from vast regions to improve our known samples to develop more reliable methods to determining time since death.
As noted above, information should come from vast areas across the world. This is due to the difference in the environment in each place. These geographically circumscribed areas have a limited range of environment factors that characterize the variables known to impact the rate of decomposition.
The Scientific Working Group for Anthropology (SWGANTH) recognized the key factors of temperature and humidity, burial substrate, burial depth, oxygen content, soil type, soil texture, soil moisture content, soil chemistry, sun exposure, rainfall, insect and scavenger activity, among others. These factors are descriptive of the highly variable conditions that exist in an outdoor context, necessitating multivariate models for estimating PMI that can be applicable to the diverse environmental conditions.
To date, the process of human decomposition has not been modeled over a broad range of environmental conditions across different geographic regions. With the help of geospatial technology geoFOR will be able to add levels of data to each case leading to a better models for estimating PMI.
Cross, P., & Simmons, T. (2010). The influence of penetrative trauma on the rate of decomposition. Journal of forensic sciences, 55(2), 295-301.
Hauther, K. A., Cobaugh, K. L., Jantz, L. M., Sparer, T. E., & DeBruyn, J. M. (2015). Estimating time since death from postmortem human gut microbial communities. Journal of forensic sciences, 60(5), 1234-1240.
Introna, F., G. Di Vella, et al. (1999). "Determination of postmortem interval from old skeletal remains by image analysis of luminol test results." Journal of Forensic Sciences 44(3): 535-538.
Mann, R. W., W. M. Bass, et al. (1990). "Time since death and decomposition of the human-body - variables and observations in case and experimental field studies." Journal of Forensic Sciences 35(1): 103-111.
McLaughlin, G. and I. K. Lednev "Potential application of Raman spectroscopy for determining burial duration of skeletal remains." Analytical and Bioanalytical Chemistry 401(8): 2511-2518.
Megyesi, M. S., S. P. Nawrocki, et al. (2005). "Using accumulated degree-days to estimate the postmortem interval from decomposed human remains." Journal of Forensic Sciences 50(3): 618-626.
Sutherland, A., Myburgh, J., Steyn, M., & Becker, P. J. (2013). The effect of body size on the rate of decomposition in a temperate region of South Africa. Forensic science international, 231(1-3), 257-262.
Vass, A. A., Bass, W. M., Wolt, J. D., Foss, J. E., & Ammons, J. T. (1992). Time since death determinations of human cadavers using soil solution. Journal of Forensic Science, 37(5), 1236-1253.
Vass, A. A. (2011). "The elusive universal post-mortem interval formula." Forensic Science International 204(1-3): 34-40.
Vass, A. A. (2012). "Odor mortis." Forensic Science International 222(1-3): 234-241.
Visual example of spatial layers used in GIS models. (