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Article

A Pilot Study of Age Estimation and Cause of Death: Insights into Skeletal Aging

by
Nicollette S. Appel
1,2,* and
Heather J. H. Edgar
1,2
1
Anthropology Department, University of New Mexico, Albuquerque, NM 87131, USA
2
New Mexico Office of the Medical Investigator, Albuquerque, NM 87102, USA
*
Author to whom correspondence should be addressed.
Forensic Sci. 2024, 4(4), 508-522; https://doi.org/10.3390/forensicsci4040034
Submission received: 1 September 2024 / Revised: 2 October 2024 / Accepted: 5 October 2024 / Published: 8 October 2024
(This article belongs to the Special Issue The Role of Genetics and the Environment on Human Variation)
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Abstract

:
Background/Objectives: Forensic anthropological age estimations are often limited by a lack of diversity in reference samples, imprecision, and, for certain populations, inaccuracy. This study aims to explore the relationship between health, as indicated by cause of death, and skeletal age estimation, with the goal of determining whether including health information can improve accuracy and precision in age estimation. Methods: Skeletal age data were collected from the Maxwell Museum Documented Skeletal Collection using the Lovejoy et al. method for the auricular surface and the Suchey-Brooks method for the pubic symphysis. All individuals had a known cause of death, which was categorized into two broad groups: disease-related and trauma-related. Cause of death category served as a proxy for health status. Results: Individuals who died from disease-related causes often fell within the upper end of the age ranges for both the auricular surface and pubic symphysis methods. In contrast, those who died from trauma-related causes tended to fall within the lower end of these age ranges. Conclusions: These results indicate that incorporating factors such as health into existing forensic age estimation methods could enhance the precision of age estimates, particularly by addressing the influence of environmental and lifestyle factors on skeletal aging.

1. Introduction

Age estimation is crucial to forensic anthropology, serving as a fundamental element in the biological profile. This profile typically includes estimations of age, sex, population affinity, and stature, all of which are useful in limiting the pool of missing persons to which unknown human remains should be compared. In addition to narrowing potential matches with missing persons, accurate and precise age estimation can provide insights into the life history of the deceased, including health, nutrition, and social status.
Traditional methods for skeletal age estimation often result in broad age ranges that can limit their utility in forensic contexts. These methods, developed primarily from reference populations that do not represent the diversity of modern cases, often fail to account for individual variability in aging processes [1,2]. Additionally, the lack of standardized protocols for integrating multiple age indicators leads to inconsistencies in age estimates [3]. These challenges highlight the need for improved methodologies that can provide more precise and reliable age estimates by considering a wider range of biological and environmental factors.
Age estimation research has largely focused on developing accurate and precise predictions of chronological age based on skeletal remains. Despite advancements in age estimation techniques, these efforts are limited due to a significant gap in understanding how skeletal age indicators and chronological age are affected by health, lifestyle, and environmental exposures. Here, we present a pilot study examining how health proxies via cause of death relate to age estimation. This research is one step towards improving the accuracy and precision of forensic analyses by integrating health factors into the age estimation process.

1.1. The Science of Aging

The process of aging is a complex progression that begins with growth and development and eventually leads to various degenerative changes. Questions about why we age, how we age, and the rate at which aging occurs continue to be central to scientific inquiry. Researchers aim to better understand health, diseases, genetics, and biology through the lens of aging. For biological anthropologists, the science of aging is essential to accurately interpret the biological and skeletal changes that inform research questions related to development, mortality, and population health. The evolutionary dynamics that underpin aging and longevity result from an interplay between genetics, life-history traits, and environmental factors [4].

1.1.1. Biological Age

In aging research, chronological age (CA) refers to the number of years, months, and days a person has been alive, serving as the baseline for other age values. Biological age (BA), on the other hand, has multiple interpretations depending on the context. BA is a physiological measure representing the rate at which an individual has physically aged [5,6,7]. In contemporary research, BA often relates to cellular-level aging, quantified through telomere shortening, DNA methylation, the epigenetic clock, and other molecular biomarkers [7,8,9]. An epigenetic clock is a mathematical model that uses DNA methylation levels in tissues and blood to estimate biological age. DNA methylation naturally occurs with age, but the rate of the epigenetic clock varies among individuals [10]. Studying the epigenome allows researchers to link the genotype and phenotype to understand how the aging process changes in response to the environment [11,12,13]. Analyses have demonstrated that BA better predicts late-life depression, overall mortality, intensive care unit mortality, and stroke-related mortality than does chronological age [14,15,16,17].
Studies of BA via the epigenetic clock have led to investigations into the factors influencing its acceleration and deceleration [18,19]. While some research has focused on population-specific analyses, examining epigenetic age differences by race (the term “race” is used because it reflects the term used in the cited article or the referenced forms), sex, or socioeconomic status [20,21,22], most studies test the predictive power of cellular changes for age-related morbidity and mortality at the individual level [23,24]. The concept of biological age has expanded beyond the cellular level to include biomarker measures at the level of organ systems [8,9,25,26,27]. Advancements in medical technology have improved our ability to collect biomarker data. High predictive value was found in CT-based body composition measures, including aortic calcification, muscle density, liver fat, and bone mineral density [28,29]. This relationship has been further illustrated in studies examining the impact of socioeconomic disadvantage on CT-based body composition measures [30].

1.1.2. Skeletal Age

Identifying characteristics of aging in the skeleton should be approached the same way as with any other organ system in the body. Skeletal age (SA) is an estimation of CA but may more closely reflect BA. Just as BA is dependent on biological, social, and environmental factors, so too is SA (Figure 1). These various factors are not mutually exclusive Discrepancies that exist between BA and CA should then also exist between SA and CA. Nawrocki (2010) explains that SA comes from morphological skeletal indicators that express “the skeleton’s continuing adaptation to biomechanical stress and participation in mineral metabolism, growth, remodeling, and disease” [31]. Because the accretion of biomechanical and physiological changes is not linear or regular, errors exist when using skeletal indicators to create an SA to estimate CA [31].
Behavior, social structures, health, stress, and the environment impact individuals throughout their lives, and the skeleton’s plastic nature means it reflects these influences. Methods developed for bioarchaeology and forensic anthropology are grounded in the understanding that degenerative changes occur over time. As people age, degenerative processes commence, and skeletal changes follow a general trajectory. However, this trajectory is not uniform, especially in older ages [32,33]. Age estimation methods could be improved if data reflecting these environmental and cultural variables could be incorporated. For example, population-specific correction factors based on research correlating environmental variables with skeletal degeneration can be created, or modifier variables that reflect socioeconomic status can be introduced into age estimation methods.

1.2. Age Estimation in Forensic Anthropology

The earliest methods of age estimation in forensic anthropology recognized the patterned trajectory of dental and skeletal development and degeneration. Juvenile age estimation methods generally rely on dental development, skeletal maturation and size, and epiphyseal union [34,35,36,37]. These processes occur at specific ages during adolescence and early adulthood, providing a patterned timeline for age estimation [37,38].
In adults, age estimation methods rely on morphological changes related to patterns of degeneration. Useful age indicators show unidirectional change with advancing age [1]; however, the rate and degree of change vary and are dependent on cultural and environmental influences throughout life, as well as individual variation [39,40]. This makes adult age estimation less straightforward than the more canalized process associated with juvenile development. Although histomorphometric methods exist [41,42,43], methods that draw on gross examination of skeletal elements are more common. These methods are as accurate as histomorphometric methods and are cost-effective and non-destructive.
The pubic symphysis has long been recognized as a region of interest to examine patterned degeneration [44,45], and continued research has worked towards refining this method, although general problems still exist, such as broad age range outputs and the results reflect the composition of the reference population [46]. Other commonly used adult age estimation methods include examination of the auricular surface [47,48,49], cranial sutures [50,51], and the sternal end of the fourth rib [52,53]. Age estimation methods continue to evolve, incorporating advancements in technology and statistical modeling. Transition Analysis integrates age-related data from various skeletal components using advanced statistical methods to generate accurate age estimates and intervals. This approach addresses the limitations of earlier methods, which relied on broad, phase-based classifications of anatomical regions [44,45]. Unlike these earlier methods, Transition Analysis accounts for the fact that aging and degenerative changes, even within a single skeletal location, do not progress at uniform rates [50,54]. Applying modern statistical methods, including Bayesian inference and machine learning algorithms, to age estimation allows for the integration of various data sources and provides probabilistic estimates, which are more robust and reliable [50,55,56,57].

1.3. Problems with Adult Age Estimation Methods

Broad age ranges produce imprecise age estimations that may hinder investigations. Additionally, a lack of standardized protocols for combining multiple age estimation methods further complicates the process. While methods like transition analysis [50] attempt to integrate various skeletal features, there is no consensus on how to systematically synthesize data from different indicators [3].
The lack of diversity in reference samples introduces weakness in age estimation methods. Traditional methods were primarily developed using skeletal collections from White/European American and Black/African American populations [45,48,52,53]. These reference samples do not adequately represent the genetic and environmental diversity found in modern forensic cases, leading to inaccuracies when these methods are applied to different populations [58,59]. Further, lifestyle, nutrition, and health status can significantly impact skeletal aging, causing deviations from the patterns observed in reference populations [60].
Tests of age estimation methods on diverse population groups and investigations of new methods confirm that rates of aging vary among groups, so population-specific standards are necessary [61,62,63,64,65]. However, practitioners continue to use the older methods because they have become standard and have been reported to be accurate [3,66]. This accuracy is misleading because precision is low. If an age range produced by any method is 40 years, there is a high probability that the individual’s chronological age will fall within the estimated range by nature of the human life span. Variations in the biological process of aging are fundamental sources of error for estimates of skeletal age [60,67]. Rather than continuing to test the accuracy of existing methods on various populations worldwide and finding they produce accurate results that are of limited value, research should shift to exploring whether incorporating population-specific underlying factors, such as social and physical environments, might improve precision.
Validation studies also demonstrate that factors such as population, geographic region, socioeconomic status, and body size influence the accuracy of age estimation methods. Studies show that methods developed for one population may not be directly applicable to another without significant adjustments, thereby affecting the reliability of age estimates in diverse forensic cases [68,69,70,71,72].

1.4. Theoretical Perspectives for Considering the Social and Physical Environments

Social experiences inscribe themselves on the body, influencing aging and health outcomes. The body serves as a canvas for social experiences, with physical health and aging directly reflecting these experiences [73,74,75]. Marginalized individuals may experience accelerated aging due to chronic stressors like discrimination and limited resources, and continuous exposure to adversity can lead to early health deterioration and faster biological aging, impacting biological age markers [76,77,78,79]. The systematic ways in which social structures harm or disadvantage individuals perpetuate health inequalities and create discrepancies in aging [80,81,82,83]. With an understanding of how the environment and social structures influence biological aging, anthropologists must consider how these same factors influence skeletal aging.
Secular change and regional variation can also impact biological profile methods. Secular change refers to the pattern of change in human growth and development over time due to factors such as improvements in nutrition, healthcare, and overall living conditions [84,85]. These changes can significantly impact the accuracy of forensic methods if they are not considered. Boldsen et al. (2002) highlighted how influences of lifestyle and environment create variation in aging [50]. Discrepancies occur from applying methods developed on historical or archaeological samples to modern individuals, and therefore contemporary populations provide the best comparative data to draw from because it reduces biases from secular change [86]. These same kinds of discrepancies may also exist across contemporary populations, making it increasingly important to update and refine forensic methods to reflect these changes.
Here, to explore the relationship between health and aging, we present a pilot study examining patterns among cause of death, estimated skeletal age, and chronological age to consider that factors other than age may be contributing to variations in skeletal age indicators.

2. Materials and Methods

2.1. Sample

The study utilized skeletal remains from the Maxwell Documented Skeleton Collection at the University of New Mexico. This collection comprises individuals acquired through pre-death donations, post-death donations by family, or through the Office of the Medical Investigator. The collection includes individual data on sex, age, population affinity, and cause of death for most donors, with recent donations also providing health and occupational information. The subsample for this research was selected from self- and family donations (n = 55).
The sample consisted of 14 females and 41 males, with ages ranging from 19 to 60 years and an average age of 44.2 years (Figure 2). The sample was limited to those under 60 to reflect a forensic sample and limit the likelihood that everyone in the sample has age-related pathologies. While the sex distribution reflects that of the Maxwell collection, the age distribution for the sample used in this study is younger than the collection’s average to facilitate the examination of age estimation discrepancies within a more specific age group. All but three individuals self-identified or were next-of-kin identified as White; thus, variations in age estimation based on race were not relevant.
Cause of death information obtained from the Office of the Medical Investigator was available for all individuals. Two broad categories were defined: disease (n = 26) and trauma (n = 29) (Table 1). The disease category includes cancer, cardiac arrest, or hypertension. It also included deaths related to alcoholism and drug use. These are included in the disease category because addiction is a disease, and long-term alcohol and drug use can lead to health issues [87]. The trauma category represents deaths caused by gunshot wounds, blunt force trauma, stab wounds, and motor vehicle accidents. Although the manner of death for these cases may vary, the primary focus here is on the cause of death, and these types of fatalities all result from traumatic events. There were five individuals with a cause of death categorized as “Other”. These were incorporated into the trauma category because they included death from acute causes such as aspiration and carbon monoxide poisoning.
For this study, cause of death is used as a proxy for health status. Those who died due to disease are assumed to have been unhealthy and succumbed to poor health. Diseases take a physiological toll on the body and affect one or many bodily systems. The chronic effects of a disease process, as well as potential treatments, may influence the aging process and subsequently impact skeletal aging. In this study, we assumed that those who died from trauma were in better health as their death was unrelated to pre-existing morbidities. Based on the documented cause of death, in no instance was the trauma potentially caused by an underlying disease or co-morbidity.

2.2. Morphological Indicators of Age

Adult ages were estimated using two methods: Lovejoy et al. (1985) for the auricular surface (AS) and Brooks and Suchey (1990) for the pubic symphysis (PS) [45,48]. The Lovejoy method for the AS involves scoring morphological features and selecting the age range that best fits the overall presentation of traits. The age ranges were assigned scores of 1 through 8. Mean ages and 95% confidence intervals for each phase came from the Osborne et al., 2004 adjustments to the Lovejoy method [49]. Similarly, the Suchey–Brooks method involves assessing various morphological features of the PS and assigning a phase from 1 to 6 that best corresponds to the observed traits. Each phase is associated with a 95% confidence interval and a mean age. For both methods, the mean age was used in the analysis. When possible, both the right and left PS and AS were scored.

2.3. Statistical Analysis

To understand how skeletal age relates to the true chronological age, the difference between the estimated mean age from the corresponding scores and chronological age was used (SA-CA). A negative value indicates the individual was older than the mean age and vice versa. Simply using accuracy rates for the estimated age ranges is not sufficient because the age ranges produced by the Lovejoy method are too narrow, and those from the Suchey–Brooks method are too broad.
All statistical analyses were performed in the computing software R, version 4.3.2 [88]. We used a Kruskal–Wallis test to determine if there were any significant differences between the right and left scores for the PS and the AS. After testing for normality, we performed an ANOVA test to test the relationship between the SA-CA values and the cause of death groups. To show the relationship between CA and SA, Z-scores were calculated to standardize the SA ranges.

3. Results

3.1. Auricular Surface and Cause of Death

There was no significant difference between the right and left scores of the AS (p = 0.484), so the left scores were used in the analysis, and the right score was used when the left could not be scored, but the right side was available. The Shapiro–Wilks test of normality indicated that all data were normally distributed. Accuracy using AS was high, with 91% of individuals falling within the respective 95% prediction interval.
There was a significant difference in SA-CA between the disease and trauma categories of death (p < 0.05) (Figure 3). For the disease category, the mean SA-CA was −1.48, while for the trauma category, the mean was 5.54. The standardized AS results indicate that chronological ages are greater than the mean for disease-related deaths and less than the mean for deaths trauma-related deaths (Figure 4).

3.2. Pubic Symphysis and Cause of Death

There was no significant difference between the right and left scores of the PS (p = 0.973 for PS), so left scores were used in the analysis, and the right score was used when the left could not be scored, but the right side was available. The Shapiro–Wilks test of normality indicated that data were normally distributed. Accuracy using PS was high, with 95% of individuals falling within the respective 95% prediction interval.
The results for the PS are also significant and in the same direction as what was seen for the AS. There was a significant difference in SA-CA between the disease and trauma categories of death (p < 0.001) (Figure 5). The mean SA-CA for those who died of disease was −8.61, while for the trauma-related deaths, the mean SA-CA was 1.07. This pattern indicates that when the cause of death was disease, the chronological age fell within the upper end of the corresponding skeletal age range (Figure 6a). For deaths related to trauma, the chronological age was, on average, within the lower end of the corresponding skeletal age range, with the chronological age being less than the mean (Figure 6b).

4. Discussion

4.1. Overview of Findings

This study examined the relationship between cause of death categories and adult age estimations. All individuals in the sample were described as White, so population affinity was not a source of variation in SA. Cause of death, categorized as disease- or trauma-related, was used as a proxy for health status. The results here show potential trends that can be further examined to test whether incorporating other environmental and lifestyle factors into age estimation methods proves to be fruitful. This work serves as an example of how we could consider other social and environmental factors, particularly when estimating age from skeletal remains. The significant patterned discrepancies in SA and CA correlated with the cause of death highlight how cause of death affects where people fall on the age distribution. The CA of individuals who died from disease-related causes was significantly skewed towards the upper end of the produced age range and was greater than the point estimate. The opposite pattern was found for those who died of trauma-related causes. The CA in this group was, on average, below the point estimate. These findings indicate a measurable relationship between the cause of death and the ranges produced by skeletal age estimation methods.
The results from the auricular surface and pubic symphysis support and complement each other in a way that further evidences the skewed distribution of CA within SA ranges by cause of death category. For the auricular surface, the results more strongly show that the CA for trauma-related deaths falls in the lower end of the age range, as indicated by the average SA-CA equaling 6.33. Here, disease-related deaths are less skewed but are still, on average, in the upper end of the age range. Conversely, for the pubic symphysis, the stronger result was for the disease category for cause of death. For this method, there is stronger evidence that the CA for disease-related death falls in the upper end of the age range, as indicated by the average SA-CA equaling −8.61. For trauma-related deaths, the average SA-CA falls in the lower end of the age range, but the data points are more centralized.
Based on theoretical frameworks on stress and aging, there was an expectation that those who were of poor health and died of disease would have shown signs of accelerated aging compared to those who died of trauma. However, this was not found. Rather, the results indicate that those who died of disease tend to fall above the point estimate, while those who died of trauma fall below the point estimate. One possible explanation for this is that the sample used here consisted only of White individuals. The concept of accelerated aging is discussed in the context of marginalization. Because this is a homogenous sample, variations in rates of aging by population group are not being observed. While these results suggest the potential for creating more precise age ranges, they also underscore the importance of extending analyses to include additional population groups.

4.2. Implications for Forensic Practice

The current study’s results have uncovered relationships between CA, estimated SA ranges, and the cause of death, which serves as a proxy for health. It may be that other biological profile methods are similarly impacted. Previous studies have found that certain forensic anthropology methods do not work for certain groups [61,62,63,64,65]. The shifts in our approaches to these methods represent a recognition of how forensic anthropology is biocultural and how cultural and environmental contexts must now be better incorporated into biological profile analyses.
For age estimation, incorporating population differences should be considered in the same way they have been for other biological profile methods [89,90,91,92,93,94,95,96,97]. Current methods have incorporated the fact that there are differences in aging between males and females, but limited analyses have examined the environmental factors that can affect skeletal aging. Various authors have noted that regional variation, lifestyle, diet, and other environmental factors influence the rate and nature of aging [47,65,98,99]. The SWGANTH standards for age estimation note that for analyzing mature remains, “factors of the environment and life history of the individual can introduce non-age-related variation in the expression of degenerative traits and thus represent a potential source of error” [100]. However, no analyses have been performed to identify if patterns of expression exist, and no adjustments have been made to incorporate these factors into age estimation methods. The complexity and variability of the aging process after growth and development have caused adult skeletal age estimation to be difficult to refine and improve [98].
There has been a growing call to incorporate social and environmental forces into forensic anthropology methods, as highlighted by this special issue and similar initiatives [87]. Much of the focus has been on demonstrating that methods do not work with the same accuracy and reliability outside the US. However, our project highlights discrepancies in age estimations related to a proxy for health within a US population.
Going into the future, forensic age estimation methods must account for both population and environmental factors, recognizing that these influences are intertwined and impact skeletal aging. Systematic errors in age estimation can have significant consequences in forensic anthropology and the broader criminal justice system. Imprecise or inaccurate age estimates can hinder investigation and identification efforts both in the US and abroad [40,101,102,103,104]. These imprecisions make it difficult to exclude large portions of the population when dealing with unknown decedents [2,102,104]. Marginalized individuals are disproportionately affected by incorrect age estimations due to a lack of consideration for embodied stress [66,105]. While cognitive and systemic biases are documented in other areas of the criminal justice system [106,107,108], biological profile estimations generally appear to be impartial [109,110]. Despite this, methodological improvements are needed to further narrow down the pool of missing persons and aid in identification.

4.3. Limitations and Future Directions

While our study provides valuable insights, it is not without limitations. The methods used for age estimation, the Brooks and Suchey (1990) method for the pubic symphysis and the Lovejoy et al. (1985) method for the auricular surface, have inherent limitations [45,48]. Both methods rely on morphological changes that can be influenced by various factors beyond age, such as physical activity, nutrition, and overall health. Additionally, the sample size, while sufficient for detecting significant trends, is relatively small, and larger studies are needed to generalize these findings to broader populations. The homogeneity of the sample is both a limitation and a strength. The lack of diversity does not accurately represent today’s population and is not a true forensic population. However, by not including race as an additional variable, an added level of complexity was removed. This allowed for testing the relationship between cause of death and skeletal age estimations alone to be tested.
Note that the cause of death is likely an imperfect proxy for health. Diseases affect the body at various rates and may or may not alter the skeleton. For trauma deaths, it is possible that these individuals did have underlying health conditions not accounted for here. Despite these limitations, the results presented here show a consistent pattern of age under- and over-estimation correlated with the cause of death category. In forensic analyses of unidentified skeletal remains, practitioners can observe visible signs of trauma or disease, which may occur simultaneously. From this, inferences can be drawn about the cause of death and the individual’s health status. Further examinations of the effects of the environment and lifestyle on skeletal aging could lead to adjustments in age estimations based on the condition of the skeleton and the presence of trauma and/or disease.
Ongoing research and the development of more refined, context-specific methods are essential. Future research should aim to refine age estimation methods by incorporating a wider range of social and environmental variables. Developing models that include health status, occupation, lifestyle, and other relevant factors could improve the accuracy of forensic age estimations. Moreover, expanding studies to include more diverse populations and larger sample sizes will help validate the findings and ensure they are broadly applicable. Additionally, efforts should focus on creating standardized protocols that integrate multiple indicators and account for individual and population variations. Improving the accuracy and reliability of age estimation methods in forensic anthropology will require a multifaceted approach that includes updating reference populations, refining existing methods, and developing new techniques tailored to diverse populations.
One potential area for future research is the impact of chronic illnesses on skeletal aging. For instance, conditions such as osteoporosis, arthritis, and other degenerative diseases can significantly alter skeletal morphology and complicate age estimations [62,111,112]. Understanding how these conditions affect skeletal features could lead to more accurate age estimation methods that account for the health status of individuals.
Another area for future exploration is the influence of occupational stress on skeletal aging. Studies have shown that individuals engaged in physically demanding occupations may exhibit different patterns of skeletal aging compared to those with less physically demanding jobs [63,113,114]. Incorporating occupational data into age estimation models could enhance the accuracy and reliability of forensic analyses.

5. Conclusions

This study demonstrates the potential of incorporating social and environmental factors into forensic age estimation methods, particularly based on the influence of health status as proxied by the cause of death. The findings reveal a significant relationship between cause of death and discrepancies in skeletal and chronological age estimations, highlighting that individuals who died from disease-related causes tend to be in the upper end of the estimated skeletal age ranges, while those who died from trauma-related causes tend to be in the lower end of estimated skeletal age ranges. This underscores the possibility of refining current forensic anthropology methods to better account for biocultural influences, such as health disparities, that affect aging. By addressing these discrepancies, forensic practice can become more precise and equitable, particularly for marginalized populations who may be disproportionately affected by current limitations in age estimation methods. The study emphasizes that both population-specific and environmental factors must be integrated into forensic analyses to enhance the precision and reliability of age estimations in forensic anthropology.

Author Contributions

Conceptualization, N.S.A. and H.J.H.E.; methodology, N.S.A. and H.J.H.E.; software, N.S.A.; validation, N.S.A. and H.J.H.E.; formal analysis, N.S.A.; investigation, N.S.A.; resources, N.S.A.; data curation, N.S.A.; writing—original draft preparation, N.S.A.; writing—review and editing, N.S.A. and H.J.H.E.; visualization, N.S.A.; supervision, H.J.H.E.; project administration, N.S.A. and H.J.H.E.; funding acquisition, not applicable. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Acknowledgments

The authors would like to thank the Maxwell Museum for allowing access to the Documented Skeletal Collection to collect data for this project.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Schematic representation of the relationship between chronological age, biological age, and skeletal age indicators. Note that positive influences can lead to the deceleration of biological age, and negative influences can lead to the acceleration of biological age. Red arrows indicate the directionality of the effect.
Figure 1. Schematic representation of the relationship between chronological age, biological age, and skeletal age indicators. Note that positive influences can lead to the deceleration of biological age, and negative influences can lead to the acceleration of biological age. Red arrows indicate the directionality of the effect.
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Figure 2. Distribution of chronological age for the sample (n = 55).
Figure 2. Distribution of chronological age for the sample (n = 55).
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Figure 3. Box plot of the difference between skeletal and chronological age (SA-CA) by cause of death for the AS. The average difference between the two groups is significant (p < 0.05).
Figure 3. Box plot of the difference between skeletal and chronological age (SA-CA) by cause of death for the AS. The average difference between the two groups is significant (p < 0.05).
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Figure 4. Z-scores for the AS show the distribution of individual chronological ages relative to mean ages (SA-CA) along a normal distribution curve: (a) individuals who died of disease; (b) individuals who died due to trauma. A Z-score of 0 represents the point estimate for the age estimation.
Figure 4. Z-scores for the AS show the distribution of individual chronological ages relative to mean ages (SA-CA) along a normal distribution curve: (a) individuals who died of disease; (b) individuals who died due to trauma. A Z-score of 0 represents the point estimate for the age estimation.
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Figure 5. Box plot of the difference between skeletal and chronological age (SA-CA) by cause of death for the PS. The average difference between the two groups is significant (p < 0.001).
Figure 5. Box plot of the difference between skeletal and chronological age (SA-CA) by cause of death for the PS. The average difference between the two groups is significant (p < 0.001).
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Figure 6. Z-scores for the PS show the distribution of individual chronological ages relative to mean ages (SA-CA) along a normal distribution curve: (a) individuals who died of disease; (b) individuals who died due to trauma. A Z-score of 0 represents the point estimate for the age estimation.
Figure 6. Z-scores for the PS show the distribution of individual chronological ages relative to mean ages (SA-CA) along a normal distribution curve: (a) individuals who died of disease; (b) individuals who died due to trauma. A Z-score of 0 represents the point estimate for the age estimation.
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Table 1. Examples of how causes of death were categorized into disease or trauma.
Table 1. Examples of how causes of death were categorized into disease or trauma.
DiseaseTrauma
ArteriosclerosisAspiration
CancerBlunt force trauma
Cardiac arrestCar accident
Drug overdoseCO poisoning
EthanolismGunshot wound
HypertensionSharp force trauma
Respiratory failureSpinal fracture
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Appel, N.S.; Edgar, H.J.H. A Pilot Study of Age Estimation and Cause of Death: Insights into Skeletal Aging. Forensic Sci. 2024, 4, 508-522. https://doi.org/10.3390/forensicsci4040034

AMA Style

Appel NS, Edgar HJH. A Pilot Study of Age Estimation and Cause of Death: Insights into Skeletal Aging. Forensic Sciences. 2024; 4(4):508-522. https://doi.org/10.3390/forensicsci4040034

Chicago/Turabian Style

Appel, Nicollette S., and Heather J. H. Edgar. 2024. "A Pilot Study of Age Estimation and Cause of Death: Insights into Skeletal Aging" Forensic Sciences 4, no. 4: 508-522. https://doi.org/10.3390/forensicsci4040034

APA Style

Appel, N. S., & Edgar, H. J. H. (2024). A Pilot Study of Age Estimation and Cause of Death: Insights into Skeletal Aging. Forensic Sciences, 4(4), 508-522. https://doi.org/10.3390/forensicsci4040034

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