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Trends in Criminal Activity, Crime Reporting, and Public Perceptions

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In the United States, crime rates have been falling sharply since their peak in the 1990s. Researchers have put forward a wide range of explanations to explain this shift. Despite the overall long-term declining trend, people's perceptions of crime have been shifting, according to various polls, in the opposite direction.

Crime is a complex, multidimensional problem. Different factors explain the large observed variability in crime rates across geographic areas and demographic groups. Ultimately, the immediate consequences of crime are highly localized, affecting the overall well-being of communities. Recent highly publicized events involving police violence have heightened concerns about crime and the criminal justice system.

To assess existing law enforcement and crime preventive policies, it is important to first understand what kind of data are available to track crime, how the data are collected, what the data's limitations are, and what the data say.

Sources of Crime Data

The two primary sources of crime data in the United States are the Uniform Crime Reporting (UCR) program, administered by the FBI, and the National Crime Victimization Survey (NCVS), conducted by the U.S. Census Bureau for the Bureau of Justice Statistics.

The UCR program compiles crime data from local law enforcement agencies. Even though participation in this program is voluntary, about 18,000 law enforcement agencies, representing 95 percent of U.S. population, are involved in it. Because local law enforcement agencies across the country do not generally follow uniform practices when classifying and recording different types of crimes, the UCR program standardizes the data collected from the agencies. The program then converts the data into crime indices.

Index crimes — that is, crimes included in the indices — are classified into two broad categories: violent crime and property crime. The former includes murder and nonnegligent manslaughter, forcible rape, robbery, and aggravated assault; the latter includes burglary, larceny-theft, motor vehicle theft, and arson.

The UCR data are widely used by the media, policymakers, and researchers to track crime behavior. The quality of the data has been improving over time; however, the program still has a number of well-known limitations. First, it includes only crimes that are officially reported. In other words, it tracks only crimes that are known to law enforcement officials. Second, it includes only crimes known by state and local law enforcement authorities; it does not consider federal crimes or crimes at certain institutions such as jails or prisons. Third, since participation is voluntary, local agencies may not consistently submit data to the program. The FBI relies on certain processes to impute missing or unreliable data, which may vary across years. The UCR warns users that the data cannot be used to make reliable comparisons across law enforcement agencies.

The NCVS is a nationally representative survey of households conducted annually throughout the year, which asks participants about themselves and whether they were victims of a crime. The survey started in the early 1970s and currently conducts about 240,000 interviews annually. The survey reports information on nonfatal personal crimes (rape or sexual assault, robbery, aggravated and simple assault, and personal larceny) and household property crimes (burglary/trespassing, motor vehicle theft, and other theft). It also includes information about certain household characteristics, such as household income and size and the race of the head of household. A key feature of this survey is that it also provides information about the number of both reported and unreported crimes. One disadvantage of the survey is that since it is designed to calculate victimization rates at the national level, it does not offer information about victimization rates at lower geographical levels.

More recently, some local governments and law enforcement agencies have started to release crime data directly to the public. The Police Data Initiative is an example of this effort. At the moment, approximately 130 local enforcement agencies publicly share data as part of this initiative. As with the UCR, caution is needed when comparing information across agencies since local law enforcement agencies may follow different practices in recording and classifying crimes.

Developments in Crime Trends

In general, crime data show a relatively sharp decline from 1990 through roughly 1999 in both violent crime and property crime, followed by a more gradual decline over the next two decades. (See chart below.)

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Most recently, the UCR data show that the property crime rate declined from 2,131 per 100,000 people in 2019 to 1,958 in 2020. The violent crime rate, however, increased from 381 to 399 per 100,000 people. The most common form of property crime was larceny-theft, followed by burglary and motor vehicle theft. Among violent crimes, aggravated assault was the most common offense, followed by robbery, rape, and murder/nonnegligent manslaughter.

It is not uncommon for the data to show unexpected year-to-year fluctuations, especially as more specific categories of crime are examined. Even after considering these types of changes, one category of violent crime that has experienced an unusual increase from 2019 to 2020 is the murder rate. The FBI reports that the murder rate rose about 30 percent during this period, the largest annual increase on record since the 1960s, when the agency started recording this kind of crime data.

The data indicate that property crimes occur about five times more frequently than violent crimes. But the frequency with which crimes occur doesn't tell the whole story. Even though property crimes are more frequent, violent crimes have a larger impact on society and are more costly. In a 2009 article in the Journal of Quantitative Criminology , Mark Cohen of Vanderbilt University and Alex Piquero of the University of Texas at Dallas performed a careful and comprehensive estimation of the costs associated with different types of crimes. These estimates include the present value of the victim's costs, costs associated with the functioning of the criminal justice system (police, courts, prisons), and the offender's productivity loss due to incarceration or other form of incapacitation. Based on their calculations, a 2017 National Bureau of Economic Research working paper by David Autor, Christopher Palmer, and Parag Pathak of the Massachusetts Institute of Technology concluded that the (weighted) average direct cost per violent crime is about $68,000, compared to $4,000 per property crime — or, equivalently, violent crimes are on average 17 times more costly than property crimes.

The NCVS corroborates that victimization rates (both violent and property) have sharply declined since the beginning of the 1990s, in line with the UCR data. But victimization rates differ among demographics and geographic areas. In general, violent and property victimization rates are higher for Blacks compared to Whites and other races (which includes Asians, Native Hawaiians, other Pacific Islanders, American Indians, Alaska Natives, and persons of two or more races). Also, they are higher in urban areas compared to suburban and rural areas. (See chart below.)

Perceptions of national crime reflected in opinion surveys, however, do not closely align with the FBI data. The latest annual crime survey, conducted by Gallup during the period Sept. 30 to Oct. 15, 2020, shows that an increasing number of people perceive that crime has increased in the United States since the beginning of the 2000s. One interesting observation is that while respondents are more likely to perceive that crime has increased at the national level, they are also less likely to perceive an increase in crime in their local areas. (See chart below.)

Within the Fifth District, the UCR data show that crime rates have followed the general declining trend. But there are wide discrepancies across states within the district. (See charts below.) Property and violent crime rates in the District of Columbia are the highest not only within the Fifth District, but also nationwide. Crime rates in South Carolina are generally above the national average. The violent crime rate in Maryland is about the same as the one observed in South Carolina, and the property crime rate has been following the national trend closely since 2005. In Virginia and West Virginia, the crime rates are lower than the national average. Violent crime rates in North Carolina track the national trend almost perfectly.

But state averages often obscure crime in specific areas within the state, especially within cities. Data for the city of Baltimore, for example, obtained from the Open Baltimore initiative, show that while the property crime rate has declined since 2011 (from 48.6 per 1,000 residents to 29.3), the violent crime rate has actually increased (from 15.1 per 1,000 residents to 24.8).

Geographic Concentration of Crime

Index crimes are not concentrated in any particular state or city. This is consistent with theory. As stated by Brendan O'Flaherty and Rajiv Sethi of Columbia University in a 2015 article in the Handbook of Regional and Urban Economics , crime is a nontradable activity, so, in principle, it is not expected to be concentrated in a specific geographic area. The idea is that more tradable activities tend to cluster spatially because they benefit from agglomeration economies; by locating near other firms in cities or industrial clusters, participants can share knowledge and have access to a larger pool of inputs. Nontradable activities, on the other hand, can only be performed locally, so they are generally more uniformly distributed across locations.

O'Flaherty and Sethi calculated indexes of crime concentration, and the indexes show that intrametropolitan concentration of crime tends to be larger than intermetropolitan concentration. In other words, the concentration of crime is relatively high within cities, but crime is not concentrated in any specific city.

The concentration of crime at certain sites in a city, typically known as "hot spots," is markedly high for crimes such as robbery and motor vehicle theft. These locations tend to be specific places such as intersections, street sections, or addresses, rather than whole neighborhoods.

In an article in Criminology in 2015, David Weisburd of George Mason University coined the term "law of crime concentration" to refer to the importance of this phenomenon in explaining the spatial patterns of criminal activities. This type of crime behavior is also relevant when designing law enforcement policies, since targeting police resources to these areas would likely have a large impact on crime reduction.

Crime Reporting in 2020

NCVS data indicate that most crimes are not reported to the police. (See chart below.) In 2019, for example, only 40.9 percent of violent crimes and 32.5 percent of household property crimes were reported to authorities. Motor vehicle theft is the crime most frequently reported (an estimated 80 percent of these crimes are reported) and theft/larceny is the least (about 27 percent), both property crimes. For violent crimes, the lowest reporting percentage is for rapes/sexual assault (34 percent) and the highest is for robbery (47 percent). (Homicide generally has a high reporting rate, but it isn't one of the crimes included in the NCVS.)

Among the main reasons why a crime was not reported, according to respondents, were fear of reprisal or "getting the offender in trouble," a feeling that police "would not or could not do anything to help," or a belief that the crime is "a personal issue or too trivial to report."

Most of the reported crimes are not solved. According to UCR data for 2019, about 45 percent of officially reported violent crimes are cleared by arrest (or by exceptional means, which include the death of the offender and other exceptional circumstances that prevented the prosecution of the offender). For property crimes, 17 percent of the offenses are cleared.

The year 2020 was atypical in many ways. COVID-19 and several high-profile events involving police violence, such as the murder of George Floyd in Minneapolis, had an effect on how people interacted. These events also affected the overall level of crime, the types of crimes, and the incentives to report crime. While the property crime rate declined from 2019, the violent crime rate actually went up. But these statistics leave open the possibility that some people may have decided in 2020 not to engage with police by reporting a crime or to report certain types of crimes and not others.

Using publicly available data for the city of Baltimore, we examined the extent to which residents changed their reporting behavior in 2020 compared to 2019. Changes in reporting are captured by the difference in the number of 911 calls between the two years. The results show that from March to August, the number of 911 calls was lower in 2020 compared to 2019, suggesting that a large number of incidents were not reported in 2020.

This change in behavior observed throughout 2020 can be attributed to a variety of reasons. Taking a closer look at the city of Baltimore, it appears that the decline in 911 calls that started at the end of March coincides with the implementation of the stay-at-home orders in Maryland. These orders stayed in place from March 30 until May 15. During this period, the number of 911 calls in 2020 was far less than in the same periods in 2019 or 2021: 7,571 fewer 911 calls were made in 2020 relative to the same period in 2019; 5,249 fewer calls were made in 2020 relative to the same period in 2021.

This pattern was observed in other cities as well. Lockdown policies implemented by states and local governments during 2020 were intended to lower the spread of COVID-19. They decreased the overall level of mobility and, as a result, the intensity of economic and social interactions taking place across communities. All this had an effect not only on the number of criminal activities, but also on the types and targets of crimes and the likelihood of reporting certain crimes.

Engagement with Police in 2020

A recent article in the American Journal of Health Economics by Lindsey Bullinger, Jillian Carr, and Analisa Packham focused precisely on this issue. The authors examined cell phone block-level activity data, 911 calls, and crime data for Chicago during the pandemic. They found that even though the announcement of the stay-at-home orders led to a decrease in total calls for police service, the share of domestic violence-related calls increased. The article also showed that domestic-related crimes officially reported to the police and arrests actually declined. Specifically, reports fell by 6.8 percent and arrests for domestic violence crimes fell by 26.4 percent relative to 2019. The authors concluded that during March and April 2020, about 1,000 cases of domestic violence crimes in Chicago were not reported to the police.

In many locations, 911 calls in 2020 remained low even after the expiration of the stay-at-home orders. Specifically, in the city of Baltimore, from May 25 to July 27, some 54,821 fewer 911 calls were made in 2020 compared to the same period in 2019 (45,750 fewer calls in 2020 relative to 2021). The drop in requests for a police response has been attributed to a change in citizens' reporting behavior after the high-profile murder of George Floyd on May 25, 2020. In the city of Baltimore, for example, public demonstrations over the death of George Floyd were particularly visible during this period. Such incidents may have affected the desire of citizens to cooperate and engage with the police.

Recent work by Desmond Ang of the Harvard Kennedy School, Panka Bencsik of the University of Chicago, Jesse Bruhn of Brown University, and Ellora Derenoncourt of Princeton University carefully examined changes in the ratio of police-related 911 calls to the number of gunshots (detected through a technology known as ShotSpotter that uses microphones scattered around different geographic areas) in eight cities: Baltimore, Cincinnati, the District of Columbia, Milwaukee, Minneapolis, New York City, Richmond (California), and San Diego. They found that this ratio declined immediately after Floyd's murder. They also found that this change in behavior is observed in both predominantly non-White and predominantly White neighborhoods nationwide. They argued that this provides evidence of a causal effect of police violence on the incentives of citizens to engage and cooperate with the police.

State and Local Spending on Prevention

The direct involvement of state and local governments in crime preventive activities is reflected in the amount of resources they devote to police protection and corrections. In 2018, state and local governments spent $121 billion on police protection, roughly 3.7 percent of direct general expenditures, or $369 per capita, and $82 billion on corrections, 2.6 percent of expenditures, or $255 per capita. Compared to other spending categories, the share spent by state and local governments on police and corrections is a little bit higher than the share devoted to highways.

During the period 1977-2018, real spending on police protection per capita increased on average by 1.5 percent annually, about the same rate as the increase in state and local direct general expenditures, and annual per capita spending on corrections increased by about 2.7 percent. As a result, police spending as a share of total spending remained fairly constant during the period, at about 3.7 percent of direct general expenditures; spending on corrections increased from 1.6 percent in 1977 to 2.6 percent in 2018 (it reached a peak of 3.3 percent in 1999 and 2000).

Most spending on police is done by local governments (about 86 percent). While state expenditures on police are mostly targeted to highway patrols, local government spending supports sheriffs' offices and police departments.

In the Fifth District, the District of Columbia spent $908 on police protection per capita and $366 on corrections per capita in 2018, leading all the other jurisdictions in both categories. South Carolina spent the least per capita in both categories. The amount spent on police protection as a percentage of direct general expenditures in Maryland, the District of Columbia, and North Carolina exceeds the U.S. average, and the share spent on corrections is higher than the U.S. average in Virginia and Maryland. More research is needed, however, in order to determine the effectiveness of spending on crime.

A regular review and assessment of existing law enforcement practices is critical to ensure their continued effectiveness. The commitment to engage in such a process would also contribute to establishing a stronger connection between citizens and law enforcement. Such evaluation requires a careful examination of the data. It is important not only to understand what the data say, but also to be aware of their limitations. Any effort by local agencies and policymakers to improve the quality of the data and also make it broadly available to the public would enhance transparency and heighten confidence in the law enforcement institutions.

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2020 • 20–1

Research department working papers, punishment and crime: the impact of felony conviction on criminal activity.

This paper examines the short-run and long-run effects of felony conviction on crime using increases in felony larceny thresholds as an exogenous, negative shock to felony conviction probability. A felony larceny threshold is the dollar value of stolen property that determines whether a larceny theft may be charged in court as a felony rather than a misdemeanor. Felony larceny threshold policy helps states govern felony convictions, thereby regulating punishment severity.

The author focuses on the theft value distribution between old and new larceny thresholds. In theory, this “response region” is where, following enactment of a higher threshold, the incentives to commit larceny of a given stolen value amount increase the most, because that crime switches from being a felony to a misdemeanor.

Implications

This paper’s findings suggest that the impact of punishment severity—namely, felony conviction probability—on criminal activity may differ by time horizon and labor market. These different effects should be taken into account when determining the optimal policies for criminal punishment. How short-run versus long-run outcomes and how focusing on low-wage versus high-wage areas affect social welfare may inform whether it is welfare-improving to increase or decrease punishment severity. That said, even in labor markets that may experience a long-run increase in larceny rates following enactment of a higher felony larceny threshold, raising the threshold likely results in savings from decreased incarceration rates that exceed the increased public cost from escalation in crime.

This paper uses increases in felony larceny thresholds as a negative shock to felony conviction probability to examine the impact of punishment severity on criminal behavior. In the theft value distribution between old and new larceny thresholds (“response region”), higher thresholds cause a 2 percent increase in the average larceny value within 120 days of enactment. However, within five years of enactment, response region average larceny values and rates decline 2 percent and 13 percent, respectively, in low-wage areas. Thus, under certain market conditions, smaller expected penalties may reduce incentives and deter crime in the long run.

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Cybercrime Victimization and Problematic Social Media Use: Findings from a Nationally Representative Panel Study

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• Published: 25 November 2021
• Volume 46 , pages 862–881, ( 2021 )

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• Eetu Marttila 1 ,
• Aki Koivula 1 &
• Pekka Räsänen 1  

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According to criminological research, online environments create new possibilities for criminal activity and deviant behavior. Problematic social media use (PSMU) is a habitual pattern of excessive use of social media platforms. Past research has suggested that PSMU predicts risky online behavior and negative life outcomes, but the relationship between PSMU and cybercrime victimization is not properly understood. In this study, we use the framework of routine activity theory (RAT) and lifestyle-exposure theory (LET) to examine the relationship between PSMU and cybercrime victimization. We analyze how PSMU is linked to cybercrime victimization experiences. We explore how PSMU predicts cybercrime victimization, especially under those risky circumstances that generally increase the probability of victimization. Our data come from nationally representative surveys, collected in Finland in 2017 and 2019. The results of the between-subjects tests show that problematic PSMU correlates relatively strongly with cybercrime victimization. Within-subjects analysis shows that increased PSMU increases the risk of victimization. Overall, the findings indicate that, along with various confounding factors, PSMU has a notable cumulative effect on victimization. The article concludes with a short summary and discussion of the possible avenues for future research on PSMU and cybercrime victimization.

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Introduction

In criminology, digital environments are generally understood as social spaces which open new possibilities for criminal activity and crime victimization (Yar, 2005 ). Over the past decade, social media platforms have established themselves as the basic digital infrastructure that governs daily interactions. The rapid and vast adaptation of social media technologies has produced concern about the possible negative effects, but the association between social media use and decreased wellbeing measures appears to be rather weak (Appel et al., 2020 ; Kross et al., 2020 ). Accordingly, researchers have proposed that the outcomes of social media use depend on the way platforms are used, and that the negative outcomes are concentrated among those who experience excessive social media use (Kross et al., 2020 ; Wheatley & Buglass, 2019 ). Whereas an extensive body of research has focused either on cybercrime victimization or on problematic social media use, few studies have focused explicitly on the link between problematic use and victimization experiences (e.g., Craig et al., 2020 ; Longobardi et al., 2020 ).

As per earlier research, the notion of problematic use is linked to excessive and uncontrollable social media usage, which is characterized by compulsive and routinized thoughts and behavior (e.g., Kuss & Griffiths, 2017 ). The most frequently used social scientific and criminological accounts of risk factors of victimization are based on routine activity theory (RAT) (Cohen & Felson, 1979 ) and lifestyle-exposure theory (LET) (Hindelang et al., 1978 ). Although RAT and LET were originally developed to understand how routines and lifestyle patterns may lead to victimization in physical spaces, they have been applied in online environments (e.g., Milani et al., 2020 ; Räsänen et al., 2016 ).

As theoretical frameworks, RAT and LET presume that lifestyles and routine activities are embedded in social contexts, which makes it possible to understand behaviors and processes that lead to victimization. The excessive use of social media platforms increases the time spent in digital environments, which, according to lifestyle and routine activities theories, tends to increase the likelihood of ending up in dangerous situations. Therefore, we presume that problematic use is a particularly dangerous pattern of use, which may increase the risk of cybercrime victimization.

In this study, we employ the key elements of RAT and LET to focus on the relationship between problematic social media use and cybercrime victimization. Our data come from high quality, two-wave longitudinal population surveys, which were collected in Finland in 2017 and 2019. First, we examine the cross-sectional relationship between problematic use and victimization experiences at Wave 1, considering the indirect effect of confounding factors. Second, we test for longitudinal effects by investigating whether increased problematic use predicts an increase in victimization experiences at Wave 2.

Literature Review

Problematic social media use.

Over the last few years, the literature on the psychological, cultural, and social effects of social media has proliferated. Prior research on the topic presents a nuanced view of social media and its consequences (Kross et al., 2020 ). For instance, several studies have demonstrated that social media use may produce positive outcomes, such as increased life satisfaction, social trust, and political participation (Kim & Kim, 2017 ; Valenzuela et al., 2009 ). The positive effects are typically explained to follow from use that satisfy individuals’ socioemotional needs, such as sharing emotions and receiving social support on social media platforms (Pang, 2018 ; Verduyn et al., 2017 ).

However, another line of research associates social media use with several negative effects, including higher stress levels, increased anxiety and lower self-esteem (Kross et al., 2020 ). Negative outcomes, such as depression (Shensa et al., 2017 ), decreased subjective well-being (Wheatley & Buglass, 2019 ) and increased loneliness (Meshi et al., 2020 ), are also commonly described in the research literature. The most common mechanisms that are used to explain negative outcomes of social media use are social comparison and fear of missing out (Kross et al., 2020 ). In general, it appears that the type of use that does not facilitate interpersonal connection is more detrimental to users’ health and well-being (Clark et al., 2018 ).

Even though the earlier research on the subject has produced somewhat contradictory results, the researchers generally agree that certain groups of users are at more risk of experiencing negative outcomes of social media use. More specifically, the researchers have pointed out that there is a group of individuals who have difficulty controlling the quantity and intensity of their use of social media platforms (Kuss & Griffiths, 2017 ). Consequently, new concepts, such as problematic social media use (Bányai et al., 2017 ) and social networking addiction (Griffiths et al., 2014 ) have been developed to assess excessive use. In this research, we utilize the concept of problematic social media use (PSMU), which is applied broadly in the literature. In contrast to evidence of social media use in general, PSMU consistently predicts negative outcomes in several domains of life, including decreased subjective well-being (Kross et al., 2013 ; Wheatley & Buglass, 2019 ), depression (Hussain & Griffiths, 2018 ), and loneliness (Marttila et al., 2021 ).

To our knowledge, few studies have focused explicitly on the relationship between PSMU and cybercrime victimization. One cross-national study of young people found that PSMU is consistently and strongly associated with cyberbullying victimization across countries (Craig et al., 2020 ) and another one of Spanish adolescents returned similar results (Martínez-Ferrer et al., 2018 ). Another study of Italian adolescents found that an individual’s number of followers on Instagram was positively associated with experiences of cybervictimization (Longobardi et al., 2020 ). A clear limitation of the earlier studies is that they focused on adolescents and often dealt with cyberbullying or harassment. Therefore, the results are not straightforwardly generalizable to adult populations or to other forms of cybercrime victimization. Despite this, there are certain basic assumptions about cybercrime victimization that must be considered.

Cybercrime Victimization, Routine Activity, and Lifestyle-Exposure Theories

In criminology, the notion of cybercrime is used to refer to a variety of illegal activities that are performed in online networks and platforms through computers and other devices (Yar & Steinmetz, 2019 ). As a concept, cybercrime is employed in different levels of analysis and used to describe a plethora of criminal phenomena, ranging from individual-level victimization to large-scale, society-wide operations (Donalds & Osei-Bryson, 2019 ). In this study, we define cybercrime as illegal activity and harm to others conducted online, and we focus on self-reported experiences of cybercrime victimization. Therefore, we do not address whether respondents reported an actual crime victimization to the authorities.

In Finland and other European countries, the most common types of cybercrime include slander, hacking, malware, online fraud, and cyberbullying (see Europol, 2019 ; Meško, 2018 ). Providing exact estimates of cybercrime victims has been a challenge for previous criminological research, but 1 to 15 percent of the European population is estimated to have experienced some sort of cybercrime victimization (Reep-van den Bergh & Junger, 2018 ). Similarly, it is difficult to give a precise estimate of the prevalence of social media-related criminal activity. However, as a growing proportion of digital interactions are mediated by social media platforms, we can expect that cybercrime victimization on social media is also increasing. According to previous research, identity theft (Reyns et al., 2011 ), cyberbullying (Lowry et al., 2016 ), hate speech (Räsänen et al., 2016 ), and stalking (Marcum et al., 2017 ) are all regularly implemented on social media. Most of the preceding studies have focused on cybervictimization of teenagers and young adults, which are considered the most vulnerable population segments (e.g., Hawdon et al., 2017 ; Keipi et al.,  2016 ).

One of the most frequently used conceptual frameworks to explain victimization is routine activity theory (RAT) (Cohen & Felson, 1979 ). RAT claims that the everyday routines of social actors place individuals at risk for victimization by exposing them to dangerous people, places, and situations. The theory posits that a crime is more likely to occur when a motivated offender, a suitable target, and a lack of capable guardians converge in space and time (Cohen & Felson, 1979 ). RAT is similar to lifestyle-exposure theory (LET), which aims to understand the ways in which lifestyle patterns in the social context allow different forms of victimization (Hindelang et al., 1978 ).

In this study, we build our approach on combining RAT and LET in order to examine risk-enhancing behaviors and characteristics fostered by online environment. Together, these theories take the existence of motivated offenders for granted and therefore do not attempt to explain their involvement in crime. Instead, we concentrate on how routine activities and lifestyle patterns, together with the absence of a capable guardian, affect the probability of victimization.

Numerous studies have investigated the applicability of LET and RAT for cybercrime victimization (e.g., Holt & Bosser, 2008 , 2014 ; Leukfeldt & Yar, 2016 ; Näsi et al., 2017 ; Vakhitova et al., 2016 , 2019 ; Yar, 2005 ). The results indicate that different theoretical concepts are operationalizable to online environments to varying degrees, and that some operationalizations are more helpful than others (Näsi et al., 2017 ). For example, the concept of risk exposure is considered to be compatible with online victimization, even though earlier studies have shown a high level of variation in how the risk exposure is measured (Vakhitova et al., 2016 ). By contrast, target attractiveness and lack of guardianship are generally considered to be more difficult to operationalize in the context of technology-mediated victimization (Leukfeldt & Yar, 2016 ).

In the next section, we will take a closer look at how the key theoretical concepts LET and RAT have been operationalized in earlier studies on cybervictimization. Here, we focus solely on factors that we can address empirically with our data. Each of these have successfully been applied to online environments in prior studies (e.g., Hawdon et al., 2017 ; Keipi et al., 2016 ).

Confounding Elements of Lifestyle and Routine Activities Theories and Cybercrime Victimization

Exposure to risk.

The first contextual component of RAT/LET addresses the general likelihood of experiencing risk situations. Risk exposure has typically been measured by the amount of time spent online or the quantity of different online activities – the hours spent online, the number of online accounts, the use of social media services (Hawdon et al., 2017 ; Vakhitova et al., 2019 ). The studies that have tested the association have returned mixed results, and it seems that simply the time spent online does not predict increased victimization (e.g., Ngo & Paternoster, 2011 ; Reyns et al., 2011 ). On the other hand, the use of social media platforms (Bossler et al., 2012 ; Räsänen et al., 2016 ) and the number of accounts in social networks are associated with increased victimization (Reyns et al., 2011 ).

Regarding the association between the risk of exposure and victimization experiences, previous research has suggested that specific online activities may increase the likelihood of cybervictimization. For example, interaction with other users is associated with increased victimization experiences, whereas passive use may protect from cybervictimization (Holt & Bossler, 2008 ; Ngo & Paternoster, 2011 ; Vakhitova et al., 2019 ). In addition, we assume that especially active social media use, such as connecting with new people, is a risk factor and should be taken into account by measuring the proximity to offenders in social media.

Proximity to Offenders

The second contextual component of RAT/LET is closeness to the possible perpetrators. Previously, proximity to offenders was typically measured by the amount of self-disclosure in online environments, such as the number of followers on social media platforms (Vakhitova et al., 2019 ). Again, earlier studies have returned inconsistent results, and the proximity to offenders has mixed effects on the risk victimization. For example, the number of online friends does not predict increased risk of cybercrime victimization (Näsi et al., 2017 ; Räsänen et al., 2016 ; Reyns et al., 2011 ). By contrast, a high number of social media followers (Longobardi et al., 2020 ) and online self-disclosures are associated with higher risk of victimization (Vakhitova et al., 2019 ).

As in the case of risk exposure, different operationalizations of proximity to offenders may predict victimization more strongly than others. For instance, compared to interacting with friends and family, contacting strangers online may be much riskier (Vakhitova et al., 2016 ). Earlier studies support this notion, and allowing strangers to acquire sensitive information about oneself, as well as frequent contact with strangers on social media, predict increased risk for cybervictimization (Craig et al., 2020 ; Reyns et al., 2011 ). Also, compulsive online behavior is associated with a higher probability of meeting strangers online (Gámez-Guadix et al., 2016 ), and we assume that PSMU use may be associated with victimization indirectly through contacting strangers.

Target Attractiveness

The third contextual element of RAT/LET considers the fact that victimization is more likely among those who share certain individual and behavioral traits. Such traits can be seen to increase attractiveness to offenders and thereby increase the likelihood of experiencing risk situations. Earlier studies on cybercrime victimization have utilized a wide selection of measures to operationalize target attractiveness, including gender and ethnic background (Näsi et al., 2017 ), browsing risky content (Räsänen et al., 2016 ), financial status (Leukfeldt & Yar, 2016 ) or relationship status, and sexual orientation (Reyns et al., 2011 ).

In general, these operationalizations do not seem to predict victimization reliably or effectively. Despite this, we suggest that certain operationalizations of target attractiveness may be valuable. Past research on the different uses of social media has suggested that provocative language or expressions of ideological points of view can increase victimization. More specifically, political activity is a typical behavioral trait that tends to provoke reactions in online discussions (e.g. , Lutz & Hoffmann, 2017 ). In studies of cybervictimization, online political activity is associated with increased victimization (Vakhitova et al., 2019 ). Recent studies have also emphasized how social media have brought up and even increased political polarization (van Dijk & Hacker, 2018 ).

In Finland, the main division has been drawn between the supporters of the populist right-wing party, the Finns, and the supporters of the Green League and the Left Alliance (Koiranen et al., 2020 ). However, it is noteworthy that Finland has a multi-party system based on socioeconomic cleavages represented by traditional parties, such as the Social Democratic Party of Finland, the National Coalition Party, and the Center Party (Koivula et al., 2020 ). Indeed, previous research has shown that there is relatively little affective polarization in Finland (Wagner, 2021 ). Therefore, in the Finnish context it is unlikely that individuals would experience large-scale victimization based on their party preference.

Lack of Guardianship

The fourth element of RAT/LET assesses the role of social and physical guardianship against harmful activity. The lack of guardianship is assumed to increase victimization, and conversely, the presence of capable guardianship to decrease the likelihood victimization (Yar, 2005 ). In studies of online activities and routines, different measures of guardianship have rarely acted as predictors of victimization experiences (Leukfeldt & Yar, 2016 ; Vakhitova et al., 2016 ).

Regarding social guardianship, measures such as respondents’ digital skills and online risk awareness have been used, but with non-significant results (Leukfeldt & Yar, 2016 ). On the other hand, past research has indicated that victims of cyber abuse in general are less social than non-victims, which indicates that social networks may protect users from abuse online (Vakhitova et al., 2019 ). Also, younger users, females, and users with low educational qualifications are assumed to have weaker social guardianship against victimization and therefore are in more vulnerable positions (e.g., Keipi et al., 2016 ; Pratt & Turanovic, 2016 ).

In terms of physical guardianship, several technical measures, such as the use of firewalls and virus scanners, have been utilized in past research (Leukfeldt & Yar, 2016 ). In a general sense, technical security tools function as external settings in online interactions, similar to light, which may increase the identifiability of the aggressor in darkness. Preceding studies, however, have found no significant connection between technical guardianship and victimization (Vakhitova et al., 2016 ). Consequently, we decided not to address technical guardianship in this study.

Based on the preceding research findings discussed above, we stated the following two hypotheses:

H1: Increased PSMU associates with increased cybercrime victimization.

H2: The association between PSMU and cybercrime victimization is confounded by factors assessing exposure to risk, proximity to offenders, target attractiveness, and lack of guardianship.

Research Design

Our aim was to analyze how problematic use of social media is linked to cybercrime victimization experiences. According to RAT and LET, cybercrime victimization relates to how individuals’ lifestyles expose them to circumstances that increase the probability of victimization (Hindelang et al., 1978 ) and how individuals behave in different risky environments (Engström, 2020 ). Our main premise is that PSMU exposes users more frequently to environments that increase the likelihood of victimization experiences.

We constructed our research in two separate stages on the basis of the two-wave panel setting. In the first stage, we approached the relationship between PSMU and cybercrime victimization cross-sectionally by using a large and representative sample of the Finnish population aged 18–74. We also analyzed the extent to which the relationship between PSMU and cybercrime victimization was related to the confounders. In the second stage of analysis, we paid more attention to longitudinal effects and tested for the panel effects, examining changes in cybercrime victimization in relation to changes in PSMU.

Participants

We utilized two-wave panel data that were derived from the first and second rounds of the Digital Age in Finland survey. The cross-sectional study was based on the first round of the survey, organized in December 2017, for a total of 3,724 Finns. In this sample, two-thirds of the respondents were randomly sampled from the Finnish population register, and one-third were supplemented from a demographically balanced online respondent pool organized by Taloustutkimus Inc. We analyzed social media users ( N  = 2,991), who accounted for 77% of the original data. The data over-represented older citizens, which is why post-stratifying weights were applied to correspond with the official population distribution of Finns aged 18–74 (Sivonen et al., 2019 ).

To form a longitudinal setting, respondents were asked whether they were willing to participate in the survey a second time about a year after the first data collection. A total of 1,708 participants expressed willingness to participate in the follow-up survey that was conducted 15 months after the first round, in March 2019. A total of 1,134 people participated in the follow-up survey, comprising a response rate of 67% in the second round.

The question form was essentially the same for both rounds of data collection.

The final two-wave data used in the second-stage of analysis mirrored on population characteristics in terms of gender (males 50.8%) and age (M = 49.9, SD  = 16.2) structures. However, data were unrepresentative in terms of education and employment status when compared to the Finnish population: tertiary level education was achieved by 44.5% of participants and only 50.5% of respondents were employed. The data report published online shows a more detailed description of the data collection and its representativeness (Sivonen et al., 2019 ).

Our dependent variable measured whether the participants had been a target of cybercrime. Cybercrime was measured with five dichotomous questions inquiring whether the respondent had personally: 1) been targeted by threat or attack on social media, 2) been falsely accused online, 3) been targeted with hateful or degrading material on the Internet, 4) experienced sexual harassment on social media, and 5) been subjected to account stealing. Footnote 1 In the first round, 159 respondents (14.0%) responded that they had been the victim of cybercrime. In the second round, the number of victimization experiences increased by about 6 percentage points, as 71 respondents had experienced victimization during the observation period.

Our main independent variable was problematic social media use (PSMU). Initially, participants’ problematic and excessive social media usage was measured through an adaptation of the Compulsive Internet Use Scale (CIUS) , which consists of 14 items ratable on a 5-point Likert scale (Meerkerk et al., 2009 ). Our measure included five items on a 4-point scale scored from 1 (never) to 4 (daily) based on how often respondents: 1) “Have difficulties with stopping social media use,” 2)”'Have been told by others you should use social media less,” 3) “Have left important work, school or family related things undone due to social media use,” 4) “Use social media to alleviate feeling bad or stress,” and 5) “Plan social media use beforehand.”

For our analysis, all five items were used to create a new three-level variable to assess respondents’ PSMU at different intensity levels. If the respondent was experiencing daily or weekly at least one of the signs of problematic use daily, PSMU was coded as at least weekly . Second, if the respondent was experiencing less than weekly at least one of the signs of problematic use, PSMU was coded as occasionally. Finally, if the respondent was not experiencing any signs of problematic use, PSMU was coded to none.

To find reliable estimates for the effects of PSMU, we controlled for general social media use , including respondents’ activity on social networking sites and instant messenger applications. We combined two items to create a new four-level variable to measure respondents’ social media use (SMU). If a respondent reported using either social media platforms (e.g., Facebook, Twitter), instant messengers (e.g., WhatsApp, Facebook Messenger) or both many hours per day, we coded their activity as high . We coded activity as medium , if respondents reported using social media daily . Third, we coded activity as low for those respondents who reported using social media only on a weekly basis. Finally, we considered activity as very low if respondents reported using platforms or instant messengers less than weekly.

Confounding variables were related to participants’ target attractiveness, proximity to offenders, and potential guardianship factors.

Target attractiveness was measured by online political activity . Following previous studies (Koiranen et al., 2020 ; Koivula et al., 2019 ), we formed the variable based on four single items: following political discussions, participating in political discussions, sharing political content, and creating political content. Participants’ activity was initially determined by means of a 5-point scale (1 = Never, 2 = Sometimes, 3 = Weekly, 4 = Daily, and 5 = Many times per day). For analysis purposes, we first separated “politically inactive” users, who reported never using social media for political activities. Second, we coded as “followers” participants who only followed but never participated in the political discussions in social media. Third, we classified as “occasional participants” those who at least sometimes participated in political activities on social media. Finally, those participants who at least weekly used social media to participate in political activities were classified as “active participants.”

Proximity to offenders was considered by analyzing contacting strangers on social media . Initially, the question asked the extent to which respondents were in contact with strangers on social media, evaluated with a 5-point interval scale, from 1 ( Not at all ) to 5 ( Very much ). For the analysis, we merged response options 1 and 2 to form value 1, and 4 and 5 to form 3. Consequently, we used a three-level variable to measure respondents’ tendency to contact strangers on social media, in which 1 = Low, 2 = Medium, and 3 = High intensity.

Lack of guardianship was measured by gender, age, education, and main activity. Respondent’s gender (1 =  Male , 2 =  Female ), age (in years), level of education, and main activity were measured. While these variables could also be placed under target attractiveness, we placed them here. This is because background characteristics the variables measure are often invisible in online environments and exist only in terms of expressed behavior (e.g., Keipi et al., 2016 ). For statistical analysis, we classified education and main activity into binary variables. Education was measured with a binary variable that implied whether the respondent had achieved at least tertiary level education or not. The dichotomization can be justified by relatively high educational levels in Finland, where tertiary education is often considered as cut-off point between educated and non-educated citizens (Leinsalu et al., 2020 ). Main activity was measured with a binary variable that differentiated unemployed respondents from others (working, retirees, and full-time students). Regarding the lack of guardianship, unemployed people are less likely to relate to informal peer-networks occurring at workplaces or educational establishments, a phenomenon that also takes place in many senior citizens’ activities. Descriptive statistics for all measurements are provided in (Table 1 ).

Analytic techniques

The analyses were performed in two different stages with STATA 16. In the cross-sectional approach we analyzed the direct and indirect associations between PSMU and cybercrime victimization. We reported average marginal effects and their standard errors with statistical significances (Table 2 .). The main effect of PSMU was illustrated in Fig.  1 by utilizing a user-written coefplot package (Jann, 2014 ).

Likelihood of cybercrime victimization according to the level of problematic social media use. Predicted probabilities with 95% confidence intervals

When establishing the indirect effects, we used the KHB-method developed by Karlson et al. ( 2012 ) and employed the khb command in Stata (Kohler et al., 2011 ). The KHB method decomposes the total effect of an independent variable into direct and indirect via a confounding / mediating variable (Karlson et al., 2012 ). Based on decomposition analysis, we reported logit coefficients for the total effect, direct effects, and indirect effects with statistical significances and confounding percentages (Table 3 .).

In the second stage, we analyzed the panel effects. We used hybrid mixed models to distinguish two time-varying factors: between-person effects and within-person effects, and predicted changes in cybercrime victimization with respect to changes in problematic social media use. We also tested how the relationship between cybercrime victimization and other time-varying variables changed over the observation period. The hybrid models were performed by using the xthybrid command (Schunck & Perales, 2017 ).

The results for our first hypothesis are presented in Fig.  1 . The likelihood of becoming a victim of cybercrime increased significantly as PSMU increased. Respondents who reported problematic use on a daily basis experienced cybercrime with a probability of more than 40%. The probability of becoming a victim was also high, 30%, if problematic use occurred weekly.

The models predicting cybercrime victimization are shown in Table 2 . In the first model (M1), PSMU significantly predicted the risk of victimization if a participant reported even occasional problematic use (AME 0.06; p  < 0.001). If the respondent reported problematic use weekly (AME 0.17; p  < 0.001) or daily (AME 0.33; p  < 0.001), his or her probability of becoming a victim was significantly higher.

The next three models (M2-M4) were constructed on the basis of variables measuring risk exposure, proximity to offenders, and target attractiveness. The second model (M2) indicates that highly intensive social media use (AME 0.19, p  < 0.001) was related to cybercrime victimization. The third (M3) model presents that those who reported low intensity of meeting strangers online had lower probability of being victims (AME -0.11, p  < 0.001) and those who reported high intensity had higher probability (AME 0.12, p  < 0.05). Finally, the fourth (M4) model suggests that political activity was related to victimization: those who reported participating occasionally (AME 0.07, p  < 0.01) and actively (AME 0.14, p  < 0.001) had higher probability of being a victim.

Next, we evaluated how different guardianship factors were related to victimization. The fifth model (M5) indicates that age, gender, and economic activity were identified as significant protective factors. According to the results, older (AME -0.01, p  < 0.001) and male (AME -0.04, p  < 0.001) participants were less likely to be targets of cybercrime. Interestingly, higher education or unemployment was not related to victimization. Finally, the fifth model also suggests that the effect of PSMU remained significant even after controlling for confounding and control variables.

We decomposed the fifth model to determine how different confounding and control variables affected the relationship between PSMU and victimization. The results of the decomposition analysis are shown in Table 3 . First, the factors significantly influenced the association between PSMU and victimization ( B  = 0.38, p  < 0.001), which means that the confounding percentage of background factors was 58.7%. However, the total effect of PSMU remained significant ( B  = 0.27, p  < 0.001). Age was the most significant factor in the association between PSMU and victimization ( B  = 0.14; p  < 0.001), explaining 36% of the total confounding percentage. Political activity was also a major contributing factor ( B  = 0.12, p  < 0.001) that explained 31.2% of the total confounding percentage. The analysis also revealed that meeting strangers online significantly confounded the relationship between PSMU and victimization ( B  = 0.7, p  < 0.001).

In the second stage, we examined the longitudinal effects of PSMU on cybercrime victimization using panel data from Finnish social media users. We focused on the factors varying in short term, that is why we also analyzed the temporal effects of SMU, contacting strangers online, and online political activity on victimization. The demographic factors that did not change over time or for which temporal variability did not vary across clusters (such as age) were not considered in the second stage.

Table 4 shows the hybrid models predicting each variable separately. The within-effects revealed that increased PSMU increased individuals’ probability of being victimized during the observation period ( B  = 0.77, p  = 0.02). Moreover, the between-effects of PSMU was significant ( B  = 2.00, p  < 0.001), indicating that increased PSMU was related to individuals’ higher propensity to be victimized over the observation period.

We could not find significant within-subject effects in terms of other factors. However, the between-effects indicated that SMU ( B  = 2.00, p  < 0.001), low intensity of meeting strangers online ( B  = -3.27, p  < 0.001), and online political participation ( B  = 2.08, p  < 0.001) distinguished the likelihood of individuals being victimized.

Over the last decade, social media has revolutionized the way people communicate and share information. As the everyday lives of individuals are increasingly mediated by social media technologies, some users may experience problems with excessive use. In prior studies, problematic use has been associated with many negative life outcomes, ranging from psychological disorders to economic consequences.

The main objective of this study was to determine whether PSMU is also linked to increased cybercrime victimization. First, we examined how PSMU associates with cybercrime victimization and hypothesized that increased PSMU associates with increased cybercrime victimization (H1). Our findings from the cross-sectional study indicated that PSMU is a notable predictor of victimization. In fact, daily reported problematic use increased the likelihood of cybercrime victimization by more than 30 percentage points. More specifically, the analysis showed that more than 40% of users who reported experiencing problematic use daily reported being victims of cybercrime, while those who never experienced problematic use had a probability of victimization of slightly over 10%.

We also examined how PSMU captures other risk factors contributing to cybercrime victimization. Here, we hypothesized that the association between PSMU and cybercrime victimization is mediated by exposure to risk, proximity to offenders, target attractiveness, and lack of guardianship (H2). The decomposition analysis indicated that confounding factors explained over 50 percent of the total effect of PSMU. A more detailed analysis showed that the association between PSMU and cybercrime victimization was related to respondents’ young age, online political activity, activity to meet strangers online, and intensity of general social media use. This means that PSMU and victimization are linked to similar factors related to routine activities and lifestyle that increase the target's attractiveness, proximity to offenders and lack of guardianship. Notably, the effect of PSMU remained significant even after controlling for the confounding factors.

In the longitudinal analysis, we confirmed the first hypothesis and found that increased PSMU was associated with increased cybercrime victimization in both within- and between-subject analyses. The result indicated a clear link between problematic use and cybercrime experiences during the observation period: as problematic use increases, so does the individual’s likelihood of becoming a victim of cybercrime. At the same time, according to the between-subject analysis, it also appears that cybercrime experiences are generally more likely to increase for those who experience more problematic use. Interestingly, we could not find within-subject effects in terms of other factors. This means, for example, that individuals' increased encounters with strangers or increased online political activity were not directly reflected in the likelihood of becoming a victim during the observation period. The between-subject analyses, however, indicated that an individual’s increased propensity to be victimized is related to higher level of social media activity, intensity of meeting strangers online, and online political activity over time.

Our findings are consistent with those of preceding research pointing to the fact that cybervictimization is indeed a notable threat, especially to those already in vulnerable circumstances (Keipi et al., 2016 ). The probabilities of cybercrime risk vary in online interactional spaces, depending on the absence and presence of certain key components suggested in our theoretical framework. Despite the seriousness of our findings, recent statistics indicate that cybercrime victimization is still relatively rare in Finland. In 2020, seven percent of Finnish Internet users had experienced online harassment, and 13 percent reported experiencing unwelcome advances during the previous three months (OSF, 2020 ). However, both forms of cybercrime victimization are clearly more prevalent among younger people and those who use social media frequently.

Cybercrime is becoming an increasingly critical threat as social media use continues to spread throughout segments of the population. Certain online activities and routinized behaviors can be considered to be particularly risky and to increase the probability of cybercrime victimization. In our study, we have identified problematic social media use as a specific behavioral pattern or lifestyle that predicts increased risk of becoming a victim of cybercrime.

Although the overall approach of our study was straightforward, the original theoretical concepts are ambiguously defined and alternative meanings have been given to them. It follows that the empirical operationalization of the concepts was not in line with some studies looking at the premises of RAT and LET framework. Indeed, different empirical measures have been employed to address the basic elements associating with risks of victimization (e.g., Hawdon et al., 2017 ; Pratt & Turanovic, 2016 ). In our investigation, we focused on selected online activities and key socio-demographic background factors.

Similarly, we need to be cautious when discussing the implications of our findings. First, our study deals with one country alone, which means that the findings cannot be generalized beyond Finland or beyond the timeline 2017 to 2019. This means that our findings may not be applicable to the highly specific time of the COVID-19 pandemic when online activities have become more versatile than ever before. In addition, although our sample was originally drawn from the national census database, some response bias probably exists in the final samples. Future research should use longitudinal data that better represent, for example, different socio-economic groups. We also acknowledge that we did not control for the effect of offline social relations on the probability of cybercrime risk. Despite these limitations, we believe our study has significance for contemporary cybercrime research.

Our study shows that PSMU heightens the risk of cybercrime victimization. Needless to say, future research should continue to identify specific activities that comprise “dangerous” lifestyles online, which may vary from one population group to another. In online settings, there are a variety of situations and circumstances that are applicable to different forms of cybercrime. For instance, lack of basic online skills regarding cybersecurity can work like PSMU.

In general, our findings contribute to the assumption that online and offline victimization should not necessarily be considered distinct phenomena. Therefore, our theoretical framework, based on RAT and LET, seems highly justified. Our observations contribute to an increasing body of research that demonstrates how routine activities and lifestyle patterns of individuals can be applied to crimes committed in the physical world, as well as to crimes occurring in cyberspace.

Data Availability

The survey data used in this study will be made available through via Finnish Social Science Data Archive (FSD, http://www.fsd.uta.fi/en/ ) after the manuscript acceptance. The data are also available from the authors on scholarly request.

Code Availability

Analyses were run with Stata 16.1. The code is also available from the authors on request for replication purposes.

1) Have you been targeted by threat or attack on social media?

2) Have you been falsely accused online?

3) Have you been targeted with hateful or degrading material on the Internet?

4) Have you experienced sexual harassment social media?

5) Has your online account been stolen or a new account made with your name without your permission?

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Marttila, E., Koivula, A. & Räsänen, P. Cybercrime Victimization and Problematic Social Media Use: Findings from a Nationally Representative Panel Study. Am J Crim Just 46 , 862–881 (2021). https://doi.org/10.1007/s12103-021-09665-2

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Insights into the link between drug use and criminality: Lifetime offending of criminally-active opiate users

Matthias pierce.

a Centre for Mental Health and Safety, University of Manchester, 4th Floor, Ellen Wilkinson Building, Oxford Road, M13 9PL, UK

Karen Hayhurst

Sheila m. bird.

b MRC Biostatistics Unit, Institute of Public Health, University Forvie Site, Robinson Way, Cambridge, CB2 0SR, UK

Matthew Hickman

c School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK

Toby Seddon

d School of Law, University of Manchester, 4.46A Williamson Building, Oxford Road, M13 9PL, UK

Graham Dunn

e Centre for Biostatistics, University of Manchester, Jean McFarlane Building (First Floor), Oxford Road, M13 9PL, UK

Associated Data

• • Over the life-course, opiate users have elevated rates of acquisitive offending.
• • This association exists prior to opiate-initiation.
• • Opiate initiation escalates the difference between opiate users and non-users.
• • This escalation is greater for females and for non-serious acquisitive offences.

We test whether the offending trajectory of those who test positive for opiates is greater than test-negative controls and whether the relationship is constant both prior to, and post, opiate initiation. We consider whether these relationships differ according to gender and offence type.

The study provides an analysis of historical offending records in adults linked to test results for opiate and cocaine metabolites. Those testing positive for opiates were linked to treatment records to retrieve data on age of opiate initiation. Rate ratios (RR) were calculated to compare opiate positive testers to opiate and cocaine negative controls, separately by gender and adjusting for age and birth cohort. Age of opiate initiation was included in a second model as a time-dependent variable. Within-subject clustering was accounted for using generalised estimating equations.

Opiate-positive cases had higher rates of offending than test-negative controls, both prior to, and post, opiate initiation. Initiation of opiate use increased the RR by 16% for males but doubled it for females. The RR increase in non-serious acquisitive crime was greater than that seen in serious crime. For males only, opiate initiation narrowed the difference in violent offending rate between cases and controls. A larger offending increase was associated with opiate initiation in female, compared to male, users.

Conclusions

For most crime categories, the difference between groups is exacerbated by opiate initiation. The findings indicate that opiate prevention initiatives might be effective in reducing offending, particularly among females.

1. Introduction

Those dependent on heroin, and other opiates, are disproportionately involved in criminal activity ( Bennett et al., 2008 ); in particular, acquisitive offending (crimes committed for financial gain) ( Bukten et al., 2011 , Pierce et al., 2015 ). The drugs-crime association is an important driver of UK policy, reflected in its prominence in the drug strategies of successive governments ( HM Government, 2008 , Home Office, 2010 ). Explanations of this association fall into three groups:

• 1. Forward causation – drug use causes crime either through the need to: (a) fund drug use through economic necessity ( Bennett et al., 2008 ); or (b) because of psychopharmacological changes precipitated by drug ingestion ( Boyum and Kleiman, 2002 , Brownstein, 2016 , White and Gorman, 2000 ).
• 2. Reverse causation – involvement with crime leads to drug use: opportunities for drug use increase with involvement in criminal behaviour ( Hammersley et al., 1989 ).
• 3. Confounding – crime and drug use share a common (set of) cause(s): there is no direct causal relationship; rather drug use and crime co-occur because of a common cause or set of causes ( Seddon, 2006 , Seddon, 2000 ).

The underlying causal mechanism(s) is likely to be more complex than these explanations suggest ( Bennett and Holloway, 2009 , Seddon, 2000 ). Our previous work has highlighted the need for longitudinal studies with a non-drug user comparison group to examine the natural history of drug use and offending ( Hayhurst et al., 2017 ). Whilst cross-sectional studies can provide information on the extent of the drug-crime association and its strength for different subgroups and offences, the aetiological debate requires longitudinal data to establish the timing of events and to gain knowledge on how the differences between users and non-users evolves over a person’s lifetime.

Current evidence about the development of drug use and offending is constrained by design flaws in published studies, particularly the absence of suitable control groups. Our recent review of the evidence base on pathways through opiate use and offending ( Hayhurst et al., 2017 ) highlighted that research has focused on comparing offending that occurs prior to the initiation of drug use with offending that occurs thereafter. A typical example is the study by Anglin and Speckart (1988) , which examined the criminal records and clinical data of male methadone patients. Most studies which make this comparison find that offending rates are substantially higher after drug-use initiation ( Hayhurst et al., 2017 ). This pre/post design fails to separate the effects of initiation from the effects of other factors which might also be related to offending, in particular, age, which correlates strongly with offending. In general population samples, offending rates tend to peak during late adolescence ( Sweeten et al., 2013 ) which coincides with the age of drug-use initiation. For example, a large proportion (45%) of users in treatment services in the North West of England report age at first use of heroin between 15 and 19 years of age ( Advisory Council on the Misuse of Drugs, 2006 ). To disentangle the age effects from those of drug-use initiation, it is crucial to control for age, using an appropriate control group. Similarly, gender is known to be a strong influence on offending trajectories and whilst some studies have shown the pre/post contrast is greater for females ( Degenhardt et al., 2013 ), the lack of adequate comparator groups limits the inferences which can be drawn.

This paper reports a retrospective cohort analysis to compare the historical offending trajectory of offenders according to drug test result. Prior analysis on this cohort considered offending rates in the two years prior to drug-test and found that testing positive for opiates was a greater predictor of excess offending than testing positive for cocaine. We therefore focus on opiate use, by comparing the historical offending trajectory of offenders who test positive for opiate use (opiate positives) with a control group who test negative for both opiate and cocaine use (test-negatives). This comparison is performed for all offences committed and for three offence categories (serious acquisitive, non-serious acquisitive, violent) whilst controlling for age and birth cohort, and separately by gender. Information about the age of first opiate use is used to consider whether the contrast between opiate positives and test-negatives is similar both before, and after, the initiation of opiate use. The following hypotheses are considered:

• 1. Opiate positives exhibit higher rates of offending than negative testers prior to opiate positives’ initiation of opiate use;
• 2. The initiation of opiate use exacerbates the level of offending compared to negative testers;
• 3. The effect of opiate-use initiation is different for males and females.
• 4. The effect of opiate-use initiation differs by crime type.

The analysis cohort was identified from those who received a saliva drug test for opiate and cocaine metabolites following arrest, as recorded by the Drug Test Record (DTR), over the period 1st April 2005 to 31st March 2009. Age at drug-use initiation was obtained for the subset also recorded in the English National Drug Treatment Monitoring System (NDTMS) over the same period. Cohort members’ complete recorded offending history (up to 31st March 2009) was extracted from the Police National Computer (PNC).

The cohort was defined from each subject’s first drug-test record which satisfied the following criteria: (1) the subject was 18–39 years old; (2) the test was completed and undisputed; and (3) the subject was charged and sanctioned following their arrest, as evidenced from a contemporaneous PNC record. This cohort has been described in detail elsewhere ( Pierce et al., 2015 ), with the modification here of a lower upper age range and the exclusion of Wales. The age range restriction was applied since the profile of individuals whose offending persists into their 40s may be atypical ( Moffitt, 1993 , Moffitt and Caspi, 2016 ). Those drug-tested in Wales were excluded because NDTMS has coverage for England only. From the analysis cohort, we define opiate-positive cases as those who, on arrest, tested positive for opiates and negative tester controls as those who tested negative for opiates and cocaine.

The DTR records a mandatory saliva test for opiate and cocaine (crack or powder form) metabolites following arrest for a ‘trigger’ offence (pre-defined as associated with problem drug use), or at the discretion of the police officer in charge of the custody area. Trigger offences are: theft; robbery; burglary; vehicle theft; supply or possession of cocaine or heroin ( Home Office, 2011 ). Data are retained on positive and negative saliva test results, test dates, reason for test and basic demographic information. Those who test positive are required to attend an initial assessment with a drugs worker who will help the user seek treatment and other support.

The PNC is an operational database recording all UK arrests that result in a criminal charge. We consider the subset which resulted in a conviction or a caution, reprimand or warning (i.e., sanctioned offences). All sanctioned offences committed by the individual were included, from age 10 (the age of criminal liability in England) up to the two weeks prior to the drug test. We excluded this two-week period to negate the effect of the specific offence which resulted in the drug test.

NDTMS records information about individuals who seek treatment for psychoactive substance-related problems by National Health Service and third-sector providers ( Marsden et al., 2009 ). It includes information about the age at which patients first used the drug they sought treatment for. We linked cases in the analysis cohort to NDTMS records for subjects treated for opioid dependence between 1st April 2005 and 31st March 2009. NDTMS has national coverage, so every subject who received drug treatment in this period should have a record. The analysis was conducted on a complete case basis and those with missing age-of-initiation were described (see Appendix A in the Supplementary material).

Linkage between datasets was based on a minimal identifier (initials, date of birth and gender). Additionally, the PNC includes a unique identifier (PNC-ID). Those minimal identifiers with multiple PNC-IDs were excluded from the analysis, as this was taken as indicating a duplicated record. All identifiers were anonymised prior to their release to the study team to ensure that features of the original data could not be discerned.

2.2. Statistical analysis

In order to compare life-course offending between opiate-positive cases and negative test controls, offence counts per individual were grouped into 1-year age bands and a generalised estimating equation (GEE) was fitted to the data. GEEs account for correlations within clustered observations; in this analysis, offence counts belonging to the same individual. We used a log-link function and included ‘time-at-risk’ as an offset, so that the model parameters are interpreted as population-averaged estimates of the log increase in offending rate associated with a unit change in the variable. The exponential of this term is interpretable as a rate ratio (RR). The model employed an exchangeable correlation structure.

The analysis considered two models. Using the whole cohort, the first model estimated the RR associated with being an opiate user, whilst controlling for age (in years: linear and quadratic terms) and birth cohort (year of birth categorised into: <1975, 1975–1979, 1980–1984, 1985+).

The second model included only those cases that had an NDTMS record. This analysis included the same variables present in the first model with the addition of the time-dependent variable ‘initiated opiate use’, which changed value from zero to one for the year where the user declared initiating opiate use, as per their NDTMS record. Within this model there are two parameters of interest: (1) being an opiate-positive case; and (2) the initiation of opiate use. In a model with both present, the first is interpreted as the RR of the change in offending, associated with being opiate positive, prior to opiate initiation; the second as the change in the RR associated with opiate initiation. Linear combinations of these parameters can be used to derive the estimated change in offending rate associated with opiate-user status, post-initiation of drug use. For example, if the RR associated with being a case is 1.5 and the effect of ‘initiation of opiate use’ is 2 then the RR comparing cases and controls prior to initiation is 1.5 and the RR post-onset of opiate use is 3.0. For ease of interpretation we include all three estimates.

The analysis considered the categories of violent and acquisitive offences, with the latter disaggregated further into ‘serious’ and ‘non-serious’ acquisitive offences according to definitions used in local government reporting ( Audit commission, 2010 ). Sub-categories which fall under serious acquisitive crimes are: burglary; robbery; theft of a vehicle; and theft from a vehicle. Those that fall under non-serious acquisitive crimes are: prostitution; theft from a person; theft from a shop; other theft; fraud and forgery; and drug supply offences. The offences that comprise these sub-categories are detailed in Appendix B (Supplementary material).

A number of those who tested positive for opiates also tested positive for cocaine. Our prior analysis ( Pierce et al., 2015 ) demonstrated that those who tested positive for both drugs had rates of offending higher than those who tested positive for opiates only. As a sensitivity analysis, we therefore consider whether the effect of opiate-use initiation was similar in those who tested positive for opiates only and those who tested positive for both drugs (see Appendix C in the Supplementary material).

3.1. Cohort description ( Table 1 )

Description of cohort by DTR test.

** Categories not mutually exclusive.

The analysis cohort consisted of 18,965 opiate-positive cases and 78,838 test-negative controls. A quarter of both groups were female. Cases were older at their drug test (p < 0.001) and younger at their first recorded offence (p < 0.001). Cases were more likely to have a conviction for a serious acquisitive offence at this date (p < 0.001) and less likely to have a conviction for a violent offence (p < 0.001).

Sixty-seven per cent of opiate-positive cases had complete data on age-of-initiation. The majority of missing data were due to cases not having a linked treatment record (see Appendix A in the Supplementary material). The median age of initiation was similar for men and women.

3.2. Offending history ( Table 2 )

Offending rates for four categories of offences.

In total, the cohort had 1.6 million sanctioned offences. For men, the rate of historical offending for opiate-positive cases was almost double that for test-negative controls (rate per year, opiate users: 1.82; non-users: 0.91; p < 0.001); the rate for opiate-positive females was more than four times that for test-negative females (opiate users: 1.38; non-users: 0.33; p < 0.001). For both male and female opiate users, the rate of offending was lower prior to initiation of opiate use compared to post-initiation. For males and females, the rate of violent and serious acquisitive offending peaked during the late teens, whilst the rate of non-serious acquisitive offences had a later peak ( Fig. 1 a and b).

Offending rates, per year by age, opiate users and non-users for: (a) male, non-serious acquisitive offences; (b) male, serious acquisitive offences; (c) male, violent offences; (d) female, non-serious acquisitive offences; (e) female, serious acquisitive offences; (f) female, violent offences.

3.3. Comparison of offending trajectory opiate-user cases vs. non-user controls ( Table 3 )

Results of Generalised Estimating Equation analysis comparing historical offending rates of opiate users and non-users using whole sample (Model 1, N = 97,803) and those with complete data on age of initiation of opiate use (Model 2, N = 91,565), separately for males and females and for four categories of offences.

See Appendix D (Supplementary material) for rate within years.

3.3.1. Model 1: change in offending trajectory

Controlling for age, age-squared and age-cohort, male opiate positive’s prior total offending rate was double that for test-negatives (Rate Ratio: 1.99, 95% CI: 1.96–2.01); for females, it was over four times greater (RR: 4.59, 95% CI: 4.48–4.69). There was a relative increase in all categories of offending associated with being opiate-positive, with a greater increase for females than for males. The greatest increase associated with being an opiate–positive was for females and for the category non-serious acquisitive offending (RR: 4.79, 95% CI: 4.66–4.91). The lowest increase was for males and for the violent offences category.

3.3.2. Model 2: change in offending trajectory accounting for initiation of drug use

The pre-initiation offending rate for male opiate-positive cases was double the rate for test-negative controls (RR = 2.00, 95% CI: 1.97–2.03), whilst the equivalent increased rate for females was 2.80 times (95% CI: 2.71–2.90). Initiation of opiate use increased the RR by 16% for males and 100% for females. Thus, the post-initiation rate was 2.32 times greater for cases than controls among males (95% CI: 2.29–2.35) and 5.61 times greater for females (95% CI: 5.47–5.75).

Both male and female cases had higher historical rates of non-serious and serious acquisitive offences prior to, and subsequent to, initiation of opiate use. For both serious and non-serious acquisitive offending categories and for both genders, initiation of opiate use increased the difference between cases and controls. Additionally, for both genders, there was a greater increase in the RR associated with initiation of opiate use for non-serious acquisitive crimes than serious crimes. In the case of violent offences, for females, the comparison between cases and controls was similar pre, and post, opiate-use initiation (RR: 2.51 and 2.61 respectively); the effect of opiate-use initiation in males was to reduce the RR (RR: 1.79 and 1.34).

We observed cohort effects; for example, controlling for age and drug-test status, later birth cohorts had higher rates of overall historical offending than earlier birth cohorts. However, this did not hold for the sub-categories of non-serious acquisitive crime, where each birth cohort had a similar rate of offending, or for serious acquisitive crime where, for men, earlier birth cohorts had a higher rate of offending.

A sensitivity analysis which separated the opiate-positive group into those that tested positive for opiates only and those that tested positive for opiates and cocaine, showed that the effect of opiate initiation was similar for both (see Appendix C in the Supplementary material).

4. Discussion

4.1. summary of main findings.

Those testing positive for opiates had substantially higher rates of prior sanctioned offending over their life-course than those testing negative for opiates and cocaine. This finding held for both males and females, whilst controlling for age and birth cohort. Findings support our four a priori hypotheses regarding offending prior to, and post, opiate-use initiation: 1) opiate–positives had higher rates of offending than test-negative controls prior to their opiate-use onset; 2) initiation of opiate use exacerbates existing levels of offending compared to controls; 3) initiation of opiate use was associated with a larger increase in the rate ratio (RR) for female than male users; 4) the effect of opiate-use initiation on historical offending differs by crime type as well as by gender.

Of particular interest is the RR reduction in violent offending associated with opiate use initiation observed in male users; while for female users, the RR was relatively unchanged. Opiate-use initiation was associated with greater elevation in non-serious (e.g., shop-lifting) than serious (e.g., burglary) acquisitive crime for both male and female users.

Our previous work demonstrated the association between opiate use and recent offending, whilst highlighting that the strength of the association varies by gender and offence type ( Pierce et al., 2015 ). The present study expands on this analysis to investigate the longitudinal relationship between opiate-use initiation and crime. The majority of research carried out to examine the association between opiate use and crime has used a single cohort, pre/post design ( Hayhurst et al., 2017 ), rather than a separate control group. Our use of offending records over the life-course, together with a suitable control group of non-using offenders, whilst also controlling for age and birth cohort, are all important design strengths. Additionally, we use a large sample size (n = 18,965 cases; n = 78,838 controls) to supply the necessary statistical power needed to detect differences differentiated by gender and sub-category of offending.

4.2. Limitations

The current study has some weaknesses. First, the use of a retrospective design limits the inferences that can be made – for instance, we cannot assess the influence that prior offending has on the likelihood of future opiate use. We are unable to hypothesise the extent to which offending prior to opiate-use initiation is associated with use of other substances, such as cannabis or alcohol, which may precede opiate use initiation ( Lessem et al., 2006 , Lynskey, 2003 ). Also, the opiate-using cohort may not be representative of opiate users in general. The cohort is sampled from individuals who received a drug test on arrest and were subsequently sanctioned; therefore, it is of greater relevance to opiate-using offenders.

The measures used are imperfect. Drug-using offenders may be more likely than non-users to be apprehended ( Bond and Sheridan, 2007 , Stevens, 2008 ) due, for example, to intoxication leading to easier identification. This may account for some of the differences detected in the current analysis, and, potentially, for differences in the period prior to initiation of opiate use, during which the likelihood of arrest may be affected by misuse of other substances, but this explanation is unlikely to account for the strength of the association observed here. Our work corresponds with previous research highlighting high levels of offending in opiate users prior to opiate-use onset ( Shaffer et al., 1987 ); suggestive of common factors underlying both behaviours. Additionally, misclassification of non-cases was evident: 7% of negative testers were linked to an NDTMS record confirming drug-user status. Cases were identified via a saliva test which, despite having high sensitivity and specificity ( Kacinko et al., 2004 ), only detects opiates used up to 24 h prior to testing( Verstraete, 2004 ) and so may not have identified less-problematic users. Any such misclassification would mean that the opiate-user and non-user group identified in this study are more similar than they would be under any ‘gold-standard’ testing procedure, meaning that the results presented are likely to be overly conservative, therefore not disputing our conclusions.

There was missing information on age of initiation for 33% of opiate positive testers; the majority because they did not have a treatment record over the data collection period. Secondary analysis of those with missing data (see Appendix A in the Supplementary material) showed that those who were not linked to NDTMS were less likely to test positive for both opiates and cocaine and were more likely to be male. Inspection of the graphs of offending rate by age group shows that those with missing linkage to NDTMS records had lower rates of offending over the life-course than those with complete information (see Appendix E in the Supplementary material). This could be because individuals who had not sought treatment were a shorter time into their using careers and not caught in a cycle of addiction and offending seen among those in this analysis. Therefore, the generalisability of these results might be affected by our focus on those individuals with a linked treatment record (75% of our cohort).

The findings of the present study are subject to unmeasured confounding. Information on important social factors, such as substance use or criminal behaviour among family members, was not available; neither was socio-economic status ( Gauffin et al., 2013 ). However, even if suitable data were available, it may be difficult to establish the temporal ordering of change in socio-economic status and drug-use initiation.

4.3. Implications and findings in relation to other evidence

Our findings are directly relevant to Government drug policy as they are derived from individuals who have persisted in both their opiate use and offending. The findings confirm the relationship between opiate use and offending observed by others ( Bennett et al., 2008 , Bukten et al., 2011 ). We were also able to demonstrate that opiate-use onset is associated with crime escalation, independent of changes which occur with age. Therefore, initiation of opiate use appears to be a crucial driver of offending; measures to reduce offending should include drug-use prevention.

Others have highlighted that onset substance use in offenders impedes the process of “maturing” out of crime described by the age-crime curve ( Hussong et al., 2004 , Ouimet and Le Blanc, 1996 , Schroeder et al., 2007 ). Greater escalation of offending, compared to controls, post-opiate initiation, was seen in female than male users. This confirms the findings of a recent review, which indicated lower offence rates pre-opiate use in females than males but a greater escalation of crime subsequent to opiate-use onset in females ( Hayhurst et al., 2017 ).

The absence of a relationship between violent crime and onset-opiate use in this study is of significance. Our previous work found a strong association between women testing positive for opiate use and recent violent offending, although such offences were only recorded in 8% of women ( Pierce et al., 2015 ). The current study indicates no apparent increase in violent offending by women associated with opiate initiation, and a relative reduction in violent crime for men. This finding tallies with previous research indicating no confirmed relationship between violent crime and onset-substance use ( Parker and Auerhahn, 1998 , White and Gorman, 2000 ).

The large impact of opiate-use initiation on non-serious acquisitive crime mirrors that of our previous work, which demonstrated a rate of shoplifting in opiate users that was between 3.5 (males) and 4.7 (females) times that of non-using offenders ( Pierce et al., 2015 ). These findings could be explained by opiate users focussing on criminal activity that generates sufficient income to support current drug use and that is within the skill set of the individual user ( James et al., 1979 ).

4.4. Further research

Previous research indicated greater increases in offending levels post-opiate use in individuals with onset of opiate use at an earlier age ( Hayhurst et al., 2017 ). This corresponds with key offending theories in demonstrating that early antisocial or delinquent behaviour is associated with a more pronounced offending trajectory ( Moffitt, 1993 ). It would be informative to examine this interaction further with the use of a control cohort. It would also be advantageous to analyse prospective, longitudinal cohorts so that information could be incorporated on those who desist in their offending and opiate use.

4.5. Conclusions

We have previously highlighted a surprising lack of high-quality research with which to delineate the nature of the relationship between drug use, in general, and opiate use, in particular, and crime. This is one of a handful of studies to employ a control group to account for the well-known relationship between age, drug use and crime. Findings indicate a more complex drugs-crime relationship than that espoused by current drug policy ( Home Office, 2010 ) with already higher than expected levels of offending in those who go on to use drugs, such as opiates, problematically and whose offending behaviour then escalates. Having a more nuanced understanding of the nature of the drugs-crime relationship is crucial to the development of policy responses underpinning decisions about how best to intervene to interrupt the pathway from onset crime to onset substance use ( Hayhurst et al., 2017 ). Findings suggest that complex interventions that target young, particularly female, offenders are required. Indeed, our findings align with the conclusions of others who have suggested that it is quite viable to identify future problematic substance users by patterns of early-life delinquent and offending behaviour, allowing for targeted intervention ( Macleod et al., 2013 ).

This research was funded as part of the Insights study by the UK Medical Research Council (MR/J013560/1). The MRC had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. The Home Office have been provided with a pre-submission version of this manuscript but have not exerted any editorial control over, or commented on, its content. Sheila Bird is funded by Medical Research Council programme number MC_U105260794.

Contributors

Millar , Pierce and Hayhurst conceived of the study. Pierce with input from Bird wrote the analysis plan. Pierce analysed the data and wrote a first draft of the manuscript. Millar , Bird and Dunn supervised data analysis. All interpreted the data, edited, and approved of the manuscript.

Conflicts of interest

Millar has received research funding from the UK National Treatment Agency for Substance Misuse and the Home Office. He has been a member of the organising committee for conferences supported by unrestricted educational grants from Reckitt Benckiser, Lundbeck, Martindale Pharma, and Britannia Pharmaceuticals Ltd, for which he received no personal remuneration. He is a member of the Advisory Council on the Misuse of Drugs. Bird holds GSK shares. She is formerly an MRC programme leader and has been elected to Honorary Professorship at Edinburgh University. She chaired Home Office’s Surveys, Design and Statistics Subcommittee (SDSSC) when SDSSC published its report on 21st Century Drugs and Statistical Science. She has previously served as UK representative on the Scientific Committee for European Monitoring Centre for Drugs and Drug Addiction. She is co-principal investigator for MRC-funded, prison-based N-ALIVE pilot Trial. Seddon has received research funding from the UK National Treatment Agency for Substance Misuse and the Home Office. Hayhurst has received grant research funding from Change, Grow, Live (CGL), a 3rd-sector provider of substance misuse services.

Acknowledgements

A number of organisations and individuals enabled access to data to support this research, including: The Home Office, The Ministry of Justice, Dr Sara Skodbo, Maryam Ahmad, Anna Richardson, Hannah Whitehead, and Nick Manton.

Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.drugalcdep.2017.07.024 .

Appendix A. Supplementary data

The following is Supplementary data to this article: