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An exploration of unsolved missing persons cases suspected of a criminal outcome: A forensic victimology approach

Published onSep 24, 2022
An exploration of unsolved missing persons cases suspected of a criminal outcome: A forensic victimology approach
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Abstract

The purpose of this study is to explore cases of missing persons suspected of being criminal. Specifically, this research aims to empirically describe the circumstances surrounding criminal disappearances and examine whether there are different subcategories within these cases using a victimological framework. The data used in this study come from an operational police database. The sample includes 155 unsolved missing person cases whose thorough investigation by the police concluded that a criminal motive remained the most likely hypothesis. These cases occurred on the entire territory of metropolitan France and overseas. Multidimensional scale analysis was used to explore the context of disappearance according to missing person characteristics, lifestyle, everyday activities, and situational exposure at the time of the disappearance. Results show that the distribution of variables in a two-dimensional space reveals four distinct different categories related to lifestyle and situational exposure: riskier lifestyle, riskier situation, safer situation, and safer lifestyle. This study is the first to empirically explore unsolved missing person cases suspected of a criminal outcome. Moreover, the framework used in this study demonstrates the importance of victimology in an investigative context.

Keywords. Missing persons, Forensic victimology, Investigations, Multidimensional scale analysis

Introduction

Every year, missing person cases mobilize important police resources (Shalev Greene & Pakes, 2014). This phenomenon is particularly broad and involves ‘anyone reported to or by police as someone whose whereabouts are unknown, whatever the circumstances of the disappearance. The person will be considered missing until located’ (Royal Canadian Mounted Police, 2015). In the UK, approximately 300,000 missing person’s incidents are reported to the police every year (Fyfe et al., 2015), while in the USA this number reached 543,018 in 2019 (Statistica, 2021). Missing Children (2021) reported that more than 250,000 children in Europe and 45,288 children in Canada went missing in 2019. It is noteworthy that among all these missing persons, more than 80% return home within 24 hours of being reported missing and 96% are found safe within the following days and months (Fyfe et al., 2015; Tarling & Burrows, 2004). However, these numbers reveal that some missing persons are never found.

Studies have shown that the majority of disappearances are voluntary (e.g., runaway, disappearing to escape financial problems, suicide) or accidental (García‐Barceló et al., 2020; Henderson et al., 2000; Payne, 1995). However, a smaller number of disappearances are involuntary and associated with a criminal act (i.e., abductions and/or violent offending leading to homicides whose victims’ bodies are never found) (Biehal et al., 2003; García‐Barceló et al., 2020; LePard et al., 2015; Taylor et al., 2019). Although the prevalence of these cases remains small in comparison to the number of annual disappearances (i.e, 1% to 4% of cases ; see Newiss, 2006; Tarling & Burrows, 2004) their management represents an important challenge for law enforcement agencies in terms of the associated costs but more importantly, the complexity of these investigations (Fyfe et al., 2015; LePard et al., 2015; Shalev Greene & Pakes, 2014).

Despite the fact that the investigation of a criminal disappearance follows a similar procedure as the one of a confirmed homicide (Fyfe et al., 2015), the absence of a body, the crime location, the forensic evidence, and the chronology makes the investigators’ task of reconstructing the crime significantly more challenging (LePard et al., 2015). In these cases, the usual way to begin the investigation is to reconstruct the environment in which the disappearance occurred by focusing on the only information available, which is the information known about the missing person (Fyfe et al., 2015; LePard et al., 2015). However, it appears that very few studies have been conducted specifically on cases of disappearance suspected of being criminal, despite the significant hurdles that these cases represent for law enforcement.

The current study aims to explore the circumstances surrounding these disappearances by focusing on missing person information from a victimological framework. While victimology has somewhat moved away from the scientific study of victimization and victims (see Fattah, 2019 about the shift from a victimology of the act to an action victimology), several authors have emphasized the importance of this approach to improve the understanding of criminal disappearances (Fyfe et al., 2015; Sarkin, 2019). The objective of this study consists of empirically describing the circumstances surrounding criminal disappearances and examine whether there are different sub-categories within these cases. In addition to providing further clarification and insights on these cases, the study may help the police to classify a disappearance earlier as being of a criminal nature and improve the efficiency of the following investigation.

Who are the missing persons?

Previous studies proposed several typologies of missing persons. One of the first classification was proposed by Payne (1995) who presented a five-category theoretical classification. The first category labeled ‘runaways’ focused on young people and adults who made an impulsive and intentional decision to leave. The second category, ‘Throwaway’, refers to children and adolescents who were thrown out of their home by parents or relatives. This category can also be applied to spouses who were forced to leave their residence due to domestic violence. The third category labeled ‘pushaways’ refers to situations where behaviors of members of the person’s environment forced them to intentionally leave (e.g., sexual or physical abuse of children, domestic violence, etc.). The fourth category labeled ‘fallaways’ includes cases where people lost contact with their family or social environment. Finally, the last category labelled ‘takeaways’ describes people forced out of contact. This category includes cases of kidnapping (i.e., sexual and/or physical violence) as well as parental abductions.

Henderson et al. (2000) proposed an empirically-based classification of circumstances surrounding missing person incidents. They identified four main categories overlapping with some of Payne’s (1995) types. These categories are: Independence/rebellion (e.g., rebellion against parental authority, responding to peer pressure, etc.), safety concerns (e.g., suicide, abduction, accident, etc.), unintentional (e.g., confusion over times/arrangements to meet, lost because of dementia, etc.), and escaping adverse consequences (e.g., avoid financial difficulties, threat of violence, etc.). Biehal et al. (2003) proposed an empirically driven classification based on a sample of 294 missing adults and 93 missing children (i.e., less than 18 years old). They identified a four-category typology of reasons for being missing: decided (e.g., commit suicide, mental health, runaway, escape for multiple problems), drifted (e.g., lost contact, etc.), unintentional (e.g., following parental divorce, dementia, accident, miscommunication), and forced (e.g., victim of crime, parental abduction, etc.). Bonny et al. (2016) also proposed an empirical classification based on the behavioral analysis of 2992 missing persons in Scotland. Using multidimensional scaling, their findings highlighted three dimensions. The first dimension labelled ‘dysfunctional’, included vulnerable individuals presenting mental health issues as well as problems of addictions. The second dimension - ‘escape’ - reflects individuals trying to escape life stressors. Finally, the ‘unintentional’ dimension concerns individuals who had no intention of disappearing. These cases are characterized by an overreaction from a relative/friends or a miscommunication/misunderstanding. Individuals characterized by unintentional disappearance are not concerned about leaving traces. This study was replicated with 341 solved cases of missing persons that occurred in Spain (García‐Barceló et al., 2020). The multidimensional scale analysis allowed García‐Barceló et al. (2020) to identify similar intentional dimensions as was found in Bonny’s et al.’s (1996) classification (i.e., intentional dysfunctional, intentional escape, unintentional accidential/drif), and one new category labeled ‘unintentional criminal’ reflecting an individual unintentionally missing related to criminal facts.

Criminal missing person cases

Only a few studies have been published on the characteristics of missing persons in a criminal context. Newiss (2004)

Newis (2004, 2005) focused on missing persons who have been victims of homicide. Using a sample of 98 cases, Newiss (2005) found that 22% of victims were aged un 14 years old, 10% between 14-17 years old, and 67% aged 18 years old and more. They noted that younger missing person did not present the highest risk of being the victim of homicide while female were the more likely to be involved in missing-homicide (Newiss, 2005). Foy (2016) provided a comparison between some demographic characteristics of missing persons in criminal and non-criminal contexts. She identified that missing persons who are females, aged between 18 and 25 years old, last seen at a public location were more likely to be associated with a criminal event. Recently, García‐Barceló et al. (2020) identified a cluster of behaviors associated with missing persons in a criminal context. They found that these missing persons presented problems related to criminality, were in a separation process or were traveling far away (i.e., being in a different country). They were also characterized by specific routine activities such as using transportation and meeting with an intimate partner. Moreover, some studies noted that lifestyle risk factors such as being homeless, sex-trade workers, transient, and socially isolated, could also be associated with missing person cases suspected of homicide (Horan & Beauregard, 2016; LePard et al., 2015; Quinet, 2007). It is important to note, however, that in previous studies several cases involving marginalized missing persons were not associated with a criminal context (Bonny et al., 2016; Huey & Ferguson, 2020). According to the study by Quinet et al. (2016), deceased persons with no known next of kin could be reported missing in the official databases in cases where no one has come to identify and claim their body.

The existing literature indicates that only a few studies have focused on disappearances in a criminal context. Moreover, when disappearances in a criminal context are addressed, it is through descriptive statistics or through larger studies looking at all types of disappearance. Thus, knowledge on disappearances in a criminal context remains vague and there is no empirical study that has tested the homogeneity or heterogeneity nature of these cases.

A forensic victimology framework

One of the main hurdles in the study of cases that remain unsolved by the police lies in the identification of relevant information. Depending on the crime committed, it is possible to use different types of information. For example, in the case of sexual assaults, only information about the offender remains unknown since the crime scene analysis and victim interviews provide precious information (see e.g., Beauregard & Bouchard, 2010; Chopin et al., 2019; Chiu & Leclerc, 2020; Chopin et al., 2020). In the homicide context, the information is more limited since only the crime scene and victim information is available (Beauregard & Martineau, 2014). In unsolved missing persons cases, the exercise becomes even more complex as only information related to the victim and the environmental context of the disappearance are available (Fyfe et al., 2015; LePard et al., 2015). To address this situation, it seems appropriate to use the forensic victimology approach developed by Turvey and his colleagues (Ferguson & Turvey, 2008; Turvey, 2008a, 2008b; Turvey & Freeman, 2017). This approach – consisting of using victim information to help criminal investigations (Ferguson & Turvey, 2008) – is based on interactionist victimological theories (i.e., lifestyle theory and routine activities theory), which allows for the understanding of victimization processes through the prism of risk exposure (Ferguson & Turvey, 2008). The interactionist victimology perspective suggests that certain victim characteristics increase their risk of being victimized. This does not imply that victims are the cause of their victimization, but that certain factors are perceived as vulnerabilities by offenders who exploit them to successfully commit their crime. First, the forensic victimology approach suggests using victim characteristics to better understand the context of victimization (e.g., gender, age, marital status) (Turvey, 2008a). Second, it is important to analyze the victim lifestyle exposure (Diaz et al., 2008). On one hand, according to the lifestyle theory (Hindelang et al., 1978), certain individual characteristics increase the risk of victimization because they place individuals in vulnerable situations (e.g., alcohol/drug use), increase their exposure to potential offenders (e.g., active social life), or expose them to a lack of protection from other people (e.g., loner lifestyle). On the other hand, the routine activities theory (Cohen & Felson, 1979) suggests that crimes occur during everyday activities that are common to a potential victim (i.e., suitable target), a motivated offender and the absence of a guardian. Both lifestyle characteristics and everyday activities are important to consider in the understanding of the victimization process (Diaz et al., 2008). Finally, it is important to analyze the victim’s situational exposure (Turvey, 2008b). The situational exposure of a person refers to the degree of vulnerability in which the environment places on them. Specifically, the geographical (i.e., distance travelled), temporal (e.g., time of the week, time of the day), and location characteristics (i.e., type of location) are components that can increase a person’s vulnerability and consequently, their victimization risk (Turvey, 2008b; Wortley & Mazerolle, 2008).

Aim of study

Police are regularly confronted with different types of disappearances. Although the majority of these disappearances occur in a non-criminal context, others are criminal in nature. An examination of the literature shows that the knowledge regarding criminal disappearances is largely superficial compared to what has been done on non-criminal disappearances. Most of the information on these cases is descriptive and there is no empirical evidence allowing to determine the homogeneous or heterogeneous nature of this specific type of cases. This lack of empirical research can be explained by 1) the difficulty for researchers to get access to sensitive data from cases that are still unsolved, 2) the limit on the type of information available due to the nature of the event, and 3) the relatively low prevalence of these cases compared to non-criminal disappearances. Nonetheless, it appears that focusing on criminal disappearances is of the utmost importance as these cases represent a significant issue for the police in terms of investigation costs and complexity. Therefore, the current study examines missing person cases in a criminal context by looking at the circumstances in which they occurred. Specifically, using a victimological framework, we aimed to determine whether it is possible to identify subtypes of criminal disappearances based on the circumstances of the cases as well as the victim characteristics. Such knowledge is necessary to adapt the police response to these cases.

Methods

Sample

The sample used in this study included 155 unsolved missing persons cases involving a criminal context. The sample was extracted from an operational police national database of violent crimes and missing persons cases which were suspected to involve a criminal outcome (i.e., homicide, abduction). All cases occurred in the French territory (i.e., the entire territory of metropolitan and overseas France) between 1975 and 2018. To be considered criminal, cases included in the sample were all investigated by the police. After a thorough investigation, the police concluded that intentional or accidental motives to explain the disappearance could be excluded and that a criminal motive remained the most likely hypothesis. Specifically, during these investigations, the criminal investigators analyzed 1) the context of the disappearance (i.e., what the persons was doing, what she was supposed to be doing), 2) the psychological condition of victims (e.g., dementia etc. such characteristics are likely to explain the accidental disappearance or suicide), 3) their previous criminal record (i.e., a criminal record that could make revenge by a third party a likely hypothesis), 4) their friendships, as well as their professional and emotional relationships (i.e., to determine if a deliberate disappearance or a suicide is a likely hypothesis). The investigators also considered 5) the financial aspects that could be involved in the disappearance of a person (i.e., debts that could motivate a deliberate disappearance) and analyzed the income of the missing person, her assets and liabilities.

Data were collected and maintained by a team of crime analysts who work with the French police. Detailed and unique information came from investigative files that were completed by criminal investigators, interviews with witnesses/acquaintances, and reports from different forensic experts. The crime analysts were familiar with how each variable was operationalized. As is the case with most official databases used for operational purposes, information related to the coding process is not available as no inter-rater reliability measurements are performed. Although it is still possible to have missing values as the information may not always be known, this was not the case with the variables examined in the current study.

Measures

Following the forensic victimology approach (see Diaz et al., 2008; Ferguson & Turvey, 2008; Turvey, 2008a, 2008b; Turvey, 2002; Turvey & Freeman, 2017), we used variables related to missing person characteristics and risk exposure. Specifically, we used 39 variables (i.e., 37 dichotomous variables coded 0 = absence, 1 = presence; and 2 continuous variables) describing missing person characteristics, lifestyle, everyday activities, and situational exposure at the time of the disappearance.

Missing person characteristics: Previous studies suggested that individual characteristics are closely associated with the victimization context (Turvey, 2008a). To explore this aspect, we used 7 variables related to missing person characteristics: 1) missing person is a female, 2) age at the time of the disappearance (continuous). From the age variable, we created three dichotomous variables: 3) children/adolescents (i.e., less than 16), 4) adults (16–64 ), 5) elderly (65 and older). Finally, we used two dichotomous variables related to the missing person marital status: 6) single, and 7) lived alone.

Missing person lifestyle risk factors: According to the victimological lifestyle theory (Hindelang et al., 1978), certain lifestyle factors increase individual’s exposure to victimization. Identification and analysis of such factors are important in an investigative perspective as they may help the police to understand the context surrounding a person’s disappearance (Fyfe et al., 2015; Turvey & Freeman, 2017). We used seven dichotomous variables to examine this aspect: 8) problems with alcohol/drugs abuse (i.e., regularly used alcohol and/or drugs), 9) active social life (i.e., regularly partying, participated in social situations and attended events where other people gather), 10) avoided social contact with others (i.e., antisocial lifestyle with few social interactions), 11) homeless/transient, 12) previous criminal convictions, 13) psychological disorders, 14) physical disabilities.

Missing person everyday activities at the time of the disappearance. Crimes occur during everyday activities (Cohen & Felson, 1979) and it is assumed that in missing person cases in a criminal context, such information is helpful to understand the disappearance (Diaz et al., 2008). Thus, we used nine variables describing missing persons’ everyday activities at the time of the disappearance: 15) involved in domestic activities (e.g., watching TV, cooking, sleeping, etc.), 16) under the supervision/care of someone (e.g., was playing, babysitting, school activity, etc.), 17) traveling alone on foot (i.e., walking, running, from a point to another), 18) traveling in a vehicle (e.g., riding in a vehicle, hitchhiking, using public transportation, etc.), 19) involved in sporting/recreational activities (e.g., camping, cycling, involved in sports club), 20) visiting someone, 21) partying (e.g., drinking in a bar, partying in a nightclub, etc.), 22) working (i.e., legal activities), 23) involved in prostitution.

Missing person situational exposure. Situational exposure is the vulnerability experienced by a person from his environment (Turvey, 2008b). To explore this aspect, we used 13 variables describing spatiotemporal and crime location characteristics. As to the temporal characteristics, we used two variables to describe the time of the week when the person was suspected of being missing: 24) during the weekend (i.e., the missing person was seen for the last time on Saturday or Sunday), and the time of the day: 25) during the night (i.e., the missing person was seen for the last time between 6 pm and 6 am). To examine the spatial context, we used one continuous variable measuring 26) the distance (i.e., Euclidean distance) between the missing person residence1 and the presumed location of the disappearance. From this variable we created five dichotomous variables: 27) less than 1 km, 28) from 1 to 10 km, 29) from 11 to 20 km, 30) from 21 to 100 km, 31) more than 100 km. Finally, we used eight variables related to the presumed disappearance location characteristics: 32) deserted place (i.e., no witness can have seen or heard something), 33) indoor location (i.e., as opposed to outdoor location), 34) residence (i.e., missing person residence, missing person residence yard/garden, common parts of a building), 35) business location (e.g., missing person work location, mall, convenience store, etc.), 36) transportation location (e.g., victim’s vehicle, taxi, school bus, city bus, subway, etc.), 37) entertainment location (e.g., nightclub, bar, casino, etc.), 38) public location (e.g., university, school, hospital, sports facilities, train station, etc.), 39) outdoor location (e.g., wooded area, hiking trail, jogging path).

Analytical Strategy

Data analysis followed a two-step process. First, to explore the specific nature of these cases, we provided univariate analyses of the variables under study. While being limited, the use of a descriptive analysis constitutes an important step allowing to formulate hypothesis for further research. Second, to determine heterogeneity among missing person case characteristics, multidimensional scale analysis (MDS) was used with all dichotomous variables. The use of MDS is appropriate to identify the structure in a set of distance measures between a single set of objects or cases and have been previously used in criminological studies (e.g., Fox & Escue, 2021; García‐Barceló et al., 2020; Lehmann et al., 2013; Sea & Beauregard, 2021). Specifically, observations are assigned to specific locations in a conceptual low-dimensional space so that the distances between points in the space match the given similarities and dissimilarities as closely as possible (Giguère, 2006; Jaworska & Chupetlovska-Anastasova, 2009; Tsogo et al., 2000). The result is a least-squares representation of the objects in that low-dimensional space which improve the understanding of data structure. We used the Proximity Scaling (PROXCAL) procedure allowing to perform multidimensional scaling of proximity data to find a least-squares representation of the objects in a low-dimensional space (Busing et al., 1997). This procedure is commonly used for dichotomous variables as it is the case in our study. As we used only dichotomous variables to compute the MDS model, Euclidean distances between the different indicators were calculated and no specific standardize process was required. Coordinates are assigned to each indicator, allowing their graphical representation on an XY axis. The representation of the different indicators on the graph makes it possible to determine which dimensions they belong to, as well as their proximity to other indicators (i.e., the greater the association between the indicators, the closer they will be on the graph). To assess the PROXSCAL multidimensional scaling model goodness of fit, we used the Standardized Residuals Sum of Squares (STRESS) measure as well as the Tucker’s Coefficient of Congruence (Kruskal, 1964; Kruskal & Whish, 1978).

Results

Table 1 presents the descriptive analysis of all cases included in the sample. Half of the missing persons in a criminal context were female (50.32%), with an average age of 36.06 (SD = 19.24) years at the time of their disappearance. Almost half of them were single (49.03%), while 18.71% lived alone. Few of them were known to abuse alcohol/drugs (10.32%), 11.61% had an active social life, or avoided social contact with others (16.13%). Some of them presented psychological disorders (16.13%) and physical disabilities (12.90%). The majority of missing persons were walking alone (55.48%) at the time of the disappearance, while some of them were involved in domestic activities (14.84%) or traveled in a vehicle (9.03%). Approximately one quarter of the missing persons disappeared during the weekend (23.23%), or at night (12.90%). The distance between the missing person residences and the presumed location of the disappearance did not exceed 10 km for most of the cases (63.87%). In half of the cases, the presumed location of the disappearance was an outdoor location (52.90%).

[Insert Table 1 Here]

Table 2 presents goodness of fit stress and fit measures of the PROXSCAL multidimensional scale analysis. The measure-of-fit for this solution, normalized raw STRESS and STRESS I according to STRESS II, produces 0.008 and 0.032 values respectively. The normalized raw STRESS coefficient varies from 0 to 1 and should be less than .05 as a good fit, which is the case for both models (Kruskal, 1964). Accordingly, these measures suggest a good to an excellent model. The STRESS 1 value confirms the best fit of the MDS model as it is less than 0.35 (see Sturrock & Rocha, 2000 for more details on thresholds). The Tucker’s φ Coefficient of Congruence 0.954 means that 95.4% of the variance in the model is explained by the two dimensions. This coefficient should be ideally more than 95% to confirm that the two-dimension representation is appropriate for the data used (Lorenzo-Seva & Ten Berge, 2006), which is the case here.

[Insert Table 2 Here]

Table 3 and Figure 1 present the proximities coordinates of the PROXSCAL MDS model. This model includes 36 dichotomous variables related to the 155 cases of missing persons used for this study. The distribution of these variables in a two-dimensional space reveals four distinct different categories related to lifestyle and situational exposure.

Category 1 (Riskier lifestyle) is characterized by cases involving adults (0.351; 0.5062), single (0.448; 0.390), and living alone (0.384; 0.367). In these cases, the missing persons were more likely to abuse alcohol/drugs (0.243;0.250), to avoid social contact with other (0.145; 0.383), and to have a homeless/transient lifestyle (0.172; 0.176). In this category, the disappearance occurred during the weekend (0.113; 0.337) at a distance greater than 100 km (0.463; 0.218) from their residence.

Category 2 (Riskier situations) includes cases of missing persons who were female (0.971; -0.082), with an active social life (0.211; -0.110), who were traveling alone on foot (0.829; -0.445) or partying (0.196; -0.086) at the time of disappearance. The disappearance occurred during the night (0.115; -0.413), at a deserted location (0.516; -0.342), outdoor (1,042; -0.265), situated at less than 1 km (0.833; -0.186) or between 21 to 100 km (0.072; -0.119) from their residence.

Category 3 (Safer situations) includes cases of missing persons who were working (-0.156; -0.112), or involved in prostitution (-0.453; -0.045). The disappearance occurred at a business (-0.167; -0.174), entertainment (-0.379; -0.066), or public location (-0.158; -0.302). Distance between the missing person residence and the disappearance location was between 11 and 20 km (-0.115; -0.401). A subgroup of cases involved children (-0.329; -0.576), who were under the supervision/care of someone (-0.361; -0.661), involved in sportive / recreative activities (-0.174; -0.534), with psychological disorders (-0.139; -0.622).

Category 4 (Safer lifestyle) includes cases of missing persons who were elderly people (-0.578; 0.421) presenting physical disabilities (-0.424; 0.293). Individuals were involved in domestic activities (-0.456; 0.602), traveling in a vehicle (-0.325; 0.071), or visiting someone (-0.302; 0.198) at the time of disappearance. The disappearance took place indoor (-0.578; 0.368), in a residence (-0.552; 0.537) or at a transportation location (-0.234; 0.261). The distance between the missing person’s residence and the place of disappearance was located between 1 and 10 km (-0.195; 0.614).

Discussion

The aim of this study was to examine the context surrounding criminal disappearances in missing persons cases. We used a sample of 155 cases of missing persons in France for which the police investigation excluded voluntary or accidental disappearances. A victimological theoretical framework was used to explore the context of these suspected crimes, where only missing person information are available. This framework provided a structure for the information around four main axes: individual characteristics, lifestyle, routine activities, and the situational characteristics of missing persons at the time of the disappearance. In order to test whether subtypes of criminal disappearances existed, MDS analysis was used. The results indicated that among a sample of criminal disappearances, it was possible to identify four distinct dimensions that were labelled as: riskier lifestyle, riskier situation, safer situation, and safer lifestyle

Various situations in criminal disappearances

Our analysis indicated that criminal disappearances occur under various circumstances. This finding is novel and contrasts with previous studies that have classified missing person cases in a criminal context in a single group (Biehal et al., 2003; Foy, 2016; García‐Barceló et al., 2020; Henderson et al., 2000; Payne, 1995). This finding is novel and contrasts with previous studies tha have classified missing person cases in a criminal context in a single group (Biehal et al., 2003; Foy, 2016; García‐Barceló et al., 2020; Henderson et al., 2000; Payne, 1995). The sample of cases that was specifically studied in this research has often been a subgroup of more global samples including several types of missing persons cases. The various existing typologies have thus considered criminal disappearances as a separate subcategory of missing persons cases such as the unintentional criminal cluster by García‐Barceló et al. (2020). They were also identified as part of a larger cluster such as the forced category by Biehal et al. (2003) or the « takeaways » category from Payne (1995). Analysis of a specific sample of criminal disappearances cases framed with victimological theories allowed us to identify that heterogeneity exists across several subtypes when considering the degree of lifestyle and situational exposure of missing persons at the time of disappearance.

Riskier lifestyle. The riskier lifestyle dimension includes situations where missing persons are adults. Their lifestyles are characterized by psychoactive substance use (i.e., alcohol and/or drugs), loneliness, and marginal lifestyles (e.g., homeless, transient). These characteristics may be related to the lifestyle theory (Hindelang et al., 1978) which suggests that certain individual characteristics increase the likelihood of a person being victimized. A marginalized lifestyle associated with recurrent substance use has already been identified as a risk factor increasing the occurrence of homicides (see Horan & Beauregard, 2016; LePard et al., 2015; Quinet, 2007). Social instability, isolation, and relationship difficulties predispose these individuals to conflict and aggression that may lead to homicide in certain situations (Horan & Beauregard, 2016). The vulnerability associated with this dimension can also be perceived as an important element influencing the decision-making process of offenders in search of victims. On the one hand, potential offenders are more likely to target vulnerable victims with a limited risk of resistance to commit their crime. On the other hand, their social isolation could be perceived by potential aggressors as a factor that would contribute to avoiding/delaying police detection (Beauregard & Martineau, 2014; 2016). It was also observed that these disappearances took place more than 100 km away from their usual area. This may increase the vulnerability of these individuals as they present no relationship to this area and may be perceived as ‘strangers’ (i.e., without links or knowledge of the area and therefore without protection).

Riskier situations. This dimension characterizes cases in which a person disappeared while involved in a risky situational context. First, the results show that these cases are characterized by disappearances occurring at deserted places and at night. These two characteristics alone constitute important environmental factors increasing the likelihood of victimization (see Boba, 2009; Wortley & Mazerolle, 2008). These two factors are also congruent with the routine activities theory (Cohen & Felson, 1979; Felson, 2008) as they reinforce the idea of increased crime risk in the absence of a capable guardian for preventing an assault or at least intervening to stop it. Interestingly, we observed two distinct subgroups of characteristics which highlight two specific situations. One group consists of people who disappear after they were partying, while the other group consists of women who disappeared near their home while walking alone in a deserted area. These two sub-dimensions highlight situations in which disappearances occurred in a risky environment that made them appear vulnerable to potential offenders. A festive activity may lead to alcohol and/or drugs consumption, which may significantly reduce the ability of potential victims to resist (Assaad & Exum, 2002; Chopin & Beauregard, 2022; Exum, 2002). In addition, traveling alone at night, in a deserted area, constitutes situational vulnerability factors that may increase the risk of victimization. In the presence of motivated offenders, victims who found themselves in these situations represent attractive targets due to their vulnerability as well as the lack of capable guardians.

Safer situations. This category of factors characterizes cases in which the disappearance did not occur in an environmentally risky situation. We observed instead that two factors were transversal: the disappearance location was a public place, located at a distance between 11 and 20 kilometers from the residence. Such a finding is congruent with García‐Barceló et al. (2020) study which found that persons last seen in a public location were more likely to be involved in a criminal disappearance. These two factors suggest that the missing person was not in an unfamiliar environment and that witnesses may have been present at the same time as the missing person, thereby limiting the risk of victimization. However, as suggested by our results, the missing persons could have presented vulnerabilities that could have been exploited by potential offenders despite unfavorable situational conditions. Thus, our findings suggested that these missing persons were more likely to be children or teenagers with psychological disorders. This combination was previously identified as particularly predictive of violent victimization (Beauregard & Chopin, 2021; Finkelhor & Asdigian, 1996). Also, we observed that sex-trade workers disappeared in this less risky situational context. This finding is congruent with previous studies that showed that persons involved in prostitution were more likely to be victim of homicide and criminal disappearance (Horan & Beauregard, 2016; LePard et al., 2015; Quinet, 2007). Prostitution, even if it takes place near a busy area or close to an entertainment district, involves the victim to be isolated at a secluded location (e.g., dark alley, a car) to provide the services, which increase her vulnerability.

Safer lifestyle. This category of characteristics includes criminal disappearances of person with less risky lifestyles. Such result contrasts the lifestyle theory (Hindelang et al., 1978) which suggests that individual characteristics contribute to the victimization. First, we observed that these cases involve elderly people, with physical disabilities, who disappeared while at home and involved in domestic activities. Elderly disappearances were previously described in the literature as accidental and explained by the presence of psychological disorders (e.g., dementia, Alzheimer’s disease, etc.) (see e.g., Bantry White & Montgomery, 2015; Bowen et al., 2011; Gerace et al., 2015). However, our findings indicate that the elderly involved in criminal disappearances did not present any psychological disorders but were more likely to have physical handicaps. These individuals were characterized by a stable lifestyle, with daily domestic activities without any risk-taking. Such a lifestyle was previously described as increasing the risk of interpersonal victimization for the elderly (Beauregard & Chopin, 2021). They constitute attractive targets from a situational point of view as they tend to spend more time at home alone and present a limited risk of opposition (Chopin & Beauregard, 2021). It has been reported that the elderly can be abused and killed during a burglary (Chopin & Beauregard, 2020; Safarik et al., 2002a, 2002b). Our findings also showed that even in a safer lifestyle, criminal disappearances could occur while the victims were visiting someone or were using a means of transportation. This result is congruent with the study by García‐Barceló et al. (2020) who found that disappearances where individuals were using transportation or meeting with an intimate partner were more likely to be criminal.

The results of this study contribute to previous research suggesting that some missing persons are victims of crime and particularly homicide (LePard et al., 2015; Newiss, 2004, 2005, 2006; Quinet, 2007; Quinet, 2011; Quinet et al., 2016). Beyond the identification of a heterogeneity of victimological patterns of missing persons cases in a criminal context, the comparison with the existing literature underlines the similarity with some homicide cases and reinforces the idea of an existing overlap between some missing persons and homicide cases.

Conclusion

This study is the first to focus specifically on the phenomenon of disappearances in a criminal context. We used a sample of 155 cases identified by the French police as suspected criminal disappearances and explored their heterogeneity based on victimological information. The results indicated four distinct categories of context characterizing criminal disappearance cases: riskier lifestyle, riskier situation, safer situation, and safer lifestyle.

Despite being interesting and innovative, this study presents methodological limitations. First, although the sample of cases was carefully selected by the police, we cannot exclude the presence of false positives. Cases we used in this study remain unsolved and it is not possible to exclude that some of them are of a non-criminal nature until they are solved. Moreover, we cannot exclude that unreported missing person cases present different patterns than those we used in this study. Second, the nature of the cases studied lead us to adopt a victimological-centered approach. Although interesting, the information available allowed us to make only partial hypotheses about the criminal context surrounding the disappearance. Finally, we cannot exclude that the use of a large number of variables for a limited sample size may reduce the rigor of the final model.

As to the practical implications of this study, the findings highlight the fact that in cases of disappearances, the police have little to work with. However, the results have shown that an appropriate use and analysis of the victimology could prove very valuable, especially in cases of criminal disappearances. Considering that in cases of disappearance time is of the utmost importance, such analysis provides a framework to better organize the available information for the police to elaborate better working hypotheses. Although in cases of missing persons all hypotheses are considered, most often the possibility of a criminal disappearance is discarded due to the low prevalence of these cases. The current study allows the circumstances of a disappearance that suggest foul play and criminal intent to be identified in the early stages of the investigation, which is crucial to increase the odds of solving the case. Considering that prior to this, investigators had little to no information to guide their classification, time and energy were spent in the exploration of all other hypotheses. It is believed that these findings may be used by investigators to improve the identification of challenging cases and facilitate investigators' decision-making on the procedure to adopt (i.e., criminal vs. accidental or voluntary context). Beyond the identification of the precise case characteristics, our study shows that the analysis of lifestyles and contexts surrounding disappearances highlight why certain contexts suggest a criminal intent related to the disappearance. Therefore, this study of the identification of specific profiles and criteria of missing persons constitutes a first step in the creation of an assessment tool to support investigators in their analysis of the suspicious circumstances surrounding cases of disappearance. This information could help investigators to more quickly recognize the contexts in which disappearances are more likely to be criminal or not. Concretely, this could facilitate the distribution of different human and logistic resources. Moreover, it could allow to suggest working hypotheses and to prioritize the investigative leads in a shorter time period.

Future studies should attempt to replicate the identified dimensions in other samples from other countries. Also, using a qualitative approach, it would be interesting to explore the details surrounding cases of criminal disappearances to examine whether the explanatory hypotheses we formulated were adequate. Finally, it seems important that future studies look at why these criminal disappearances remain unsolved by investigating the characteristics of the cases but also how these investigations were conducted.

References

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Tables and Figure

Table 1. Descriptive analysis (N=155)

 

n=

%

Victim characteristics

 

 

Sex: Female

78

50.32

Age (continuous)

36.06a; 34b [SD=19.24; range = 5-83]

Children/Adolescents (less than 16 yo)

17

10.97

Adults (16-64 yo)

122

78.71

Elderly (65 yo and more)

16

10.32

Marital status: Single

76

49.03

Living alone

29

18.71

Victim lifestyle risk factors

 

 

Alcohol/drugs abuses

16

10.32

Active social life

18

11.61

Avoid social contact with other

25

16.13

Homeless / Transient

13

8.39

Previous criminal convictions

2

1.29

Psychological disorders

25

16.13

Physical disabilities

20

12.90

Victim routine activities at the time of the disappearance

 

Domestic activities

23

14.84

Under the supervision / care of someone

4

2.58

Travelled on foot

86

55.48

Travelled in a vehicle

14

9.03

Sportive / recreative activities

4

2.58

Was visiting someone

6

3.87

Partying

6

3.87

Was working

4

2.58

Was prostituting

6

3.87

Victim situational exposure

 

 

During the week-end

36

23.23

During the night

20

12.90

Distance between the victim's residence and the presumed location of the disappearance (continuous)

165.98a; 2.74b [SD=649.86; range = 0-6538.18]

Less than 1 km

60

38.71

From 1 to 10 km

39

25.16

From 11 to 20 km

12

7.74

From 21 to 100 km

13

8.39

More than 100 km

32

20.65

Presumed location of the disappearance characteristics

 

 

In a deserted place

24

15.48

Indoor

22

14.19

Residence

39

25.16

Business location

4

2.58

Transportation location

17

10.97

Entertainment location

5

3.23

Public location

8

5.16

Outdoor location

82

52.90

Notes. a represents the mean, b represents the median


Table 2. PROXSCAL MDS Goodness of Fit Stress and Fit Measures

Normalized Raw Stress

.008

Stress-I

.032a

Stress-II

.064a

S-Stress

.001b

Dispersion Accounted For (D.A.F.)

.913

Tucker's Coefficient of Congruence

.954

a Optimal scaling factor = 1.094. b Optimal scaling factor = .907

 

Table 3. PROXSCAL MDS Proximities coordinates (N=155)

 

 

1

2

Category 1 :Riskier lifestyle

Adults (16-64 yo)

0.351

0.506

 

Marital status: Single

0.448

0.39

 

Living with nobody

0.384

0.367

 

Alcohol/drugs abuses

0.243

0.25

 

Avoid social contact with other

0.145

0.383

 

Homeless / Transient

0.172

0.176

 

During the week-end

0.113

0.337

 

More than 100 km

0.463

0.218

Category 2 :Riskier situation

Sex: Female

0.971

-0.082

 

Active social life

0.211

-0.11

 

Travelled on foot

0.829

-0.445

 

Partying

0.196

-0.086

 

During the night

0.115

-0.413

 

In a deserted place

0.516

-0.342

 

Outdoor location

1.042

-0.265

 

Less than 1 km

0.833

-0.186

 

From 21 to 100 km

0.072

-0.119

Category 3 : Safer situation

Psychological disorders

-0.139

-0.622

 

Under the supervision / care of someone

-0.361

-0.661

 

Sportive / recreative activities

-0.174

-0.534

 

Was working

-0.156

-0.112

 

Was prostituting

-0.453

-0.045

 

Business location

-0.167

-0.174

 

Entertainment location

-0.379

-0.066

 

Public location

-0.158

-0.302

 

From 11 to 20 km

-0.115

-0.401

 

Children/Adolescents

-0.329

-0.576

Category 4 : Safer lifestyle

Elderly (65 yo and more)

-0.578

0.421

 

Physical disabilities

-0.424

0.293

 

Domestic activities

-0.456

0.602

 

Travelled in a vehicle

-0.325

0.071

 

Was visiting someone

-0.302

0.198

 

Indoor

-0.578

0.368

 

Residence

-0.552

0.537

 

Transportation location

-0.234

0.261

 

From 1 to 10 km

-0.195

0.614


Figure 1. PROXSCAL MDS plot of criminal missing person case characteristics (N=155)

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