Identifying Factors That Help Improve Existing Decomposition-Based PMI Estimation Methods

• NCJ Number: 310397
• Journal: Journal of Forensic Sciences Volume: Online Dated: 2025
• Author(s): Anna-Maria Nau; Phillip Ditto; Dawnie Wolfe Steadman; Audris Mockus
• Date Published: April 2025
• Length: 12 pages
• Grant Number(s): 15PNIJ-21-GG-04161-SLFO; 2018-DU-BX-0181
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical)

Annotation

In this study, researchers aim to identify factors that help improve existing decomposition-based postmortem interval (PMI) estimation methods.

Abstract

This study of postmortem interval (PMI) estimation methods found that significant improvements in accuracy can be achieved using readily available predictors. Accurately assessing the postmortem interval (PMI) remains a challenging task in forensic science. Existing regression models use the decomposition score to predict the PMI or accumulated degree days (ADD) but are often imprecise and rely on small sample sizes. This study explored whether a more accurate outdoor PMI estimation models can be constructed using (a) a larger sample, (b) more sophisticated statistical models, and (c) additional predictors derived from demographic and environmental factors. Using a sample of 213 human subjects from a human decomposition photographic dataset, researchers evaluated existing outdoor PMI and ADD formulae and also developed more sophisticated models that incorporate additional predictors. Models using the total decomposition score (TDS), demographic factors (age, sex, and BMI), and weather-related factors (season and humidity history) reduced PMI and ADD prediction errors by over 50%. The best PMI model, incorporating TDS, demographic, and weather predictors, achieved an adjusted R-squared of 0.42 and an RMSE of 0.76. It had a 15% lower RMSE than the TDS-only model to predict PMI and a 54% lower RMSE than the pre-existing PMI formula. Similarly, the best ADD model, using the same predictors, achieved an adjusted R-squared of 0.54 and an RMSE of 0.73. It had a 10% lower RMSE than the TDS-only model to predict the ADD and a 55% lower RMSE than the pre-existing ADD formula. (Published Abstract Provided)