This study first sought to estimate the local Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5 ) prevalence of various substance use disorders (SUDs) and psychiatric conditions among a sample of male county jail inmates ( N = 200) from 2016 data. The observed patterns in prevalence and internal consistency for the various conditions among a subgroup of inmates with a DSM-5 moderate-severe SUD diagnosis ( n = 149) were then compared to a comparable sample of Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV ) substance-dependent inmates derived from 2008 data collected in an adjacent county jail ( N = 176) using a nearly identical structured clinical interview. Results revealed 87.0% of inmates in the total 2016 sample met criteria for any DSM-5 SUD. Despite similar methodology, comparable sample populations, and county jails in the same geographic region, there were marked differences between studies with respect to the prevalence of certain SUDs over an 8-year period. Conversely, 2016 prevalence rates for the co-occurring conditions were within 1% to 7% points of the rates evidenced in 2008.

Co-occurring substance use disorders (SUDs) and psychiatric conditions are particularly prevalent among individuals involved in the criminal justice system. Although prevalence estimates among correctional populations are variable and largely influenced by the specific methodologies and settings sampled, considerably higher rates of SUDs and psychiatric conditions, both independently and in combination, have consistently been observed across various correctional settings relative to the U.S. general adult population (James & Glaze, 2006; Karberg & James, 2005; Peters, Wexler, & Lurigio, 2015; Substance Abuse and Mental Health Services Administration [SAMHSA], 2015). Specifically, studies have consistently found that two-thirds or more of inmates have a current SUD (Gunter et al., 2008; Karberg & James, 2005; Peters, Greenbaum, Edens, Carter, & Ortiz, 1998), and jail inmates with a psychiatric disorder have higher rates of co-occurring SUDs relative to those without a psychiatric disorder (76% vs. 53%, respectively; James & Glaze, 2006). A large body of research also indicates that co-occurring disorders have been linked to a number of adverse outcomes among offenders (e.g., Baillargeon et al., 2010; Constantine et al., 2010; Durose, Cooper, & Snyder, 2014; Sadeh & McNiel, 2014; Wilson, Draine, Hadley, Metraux, & Evans, 2011; Wood, 2011), and inmates with both a SUD and a psychiatric condition have been found to be significantly more likely to recidivate compared to both inmates with a SUD only (Messina, Burdon, Hagopian, & Prendergast, 2004) and inmates with a non-SUD psychiatric condition only (Hartwell, 2004).

Despite the public health and safety significance of accurately identifying inmates with co-occurring disorders, previous work examining prevalence estimates among correctional populations is often hindered by a number of methodological limitations related to the assessment and identification of SUDs. Most notably, SUD prevalence estimates have generally been based on findings from brief screening instruments for either a single substance (e.g., CAGE, Ewing, 1984; Alcohol Use Disorders Identification Test [AUDIT], Saunders, Aasland, Babor, de la Fuente, & Grant, 1993) or those that group all drugs together (e.g., Drug Abuse Screening Test [DAST], Skinner, 1982) as opposed to structured diagnostic interviews. The fact that there can be various combinations of SUD severity levels as defined by the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association [APA], 2013) across different substances suggests that simple or generic screens are insufficient in identifying true prevalence rates.

Even among studies that have used a comprehensive diagnostic assessment interview to assign Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) SUD diagnoses, many have failed to differentiate between diagnoses of dependence versus abuse, and even fewer have specified substance-specific drug use disorder diagnoses in that studies often group all drugs together in a generic drug use disorder category (e.g., Binswanger et al., 2010; Gunter et al., 2008; Karberg & James, 2005). Combining all severity levels of SUD ignores possible qualitative as well as quantitative distinctions. This would be analogous to studying diabetes without distinguishing whether one is considering type 1 versus those with type 2 diabetes given the distinctions between the two conditions. In addition, conclusions derived from some previous studies linking co-occurring psychiatric conditions to substance use have been based on frequency estimates of substance use (past month, year, etc.), or have often been derived from samples of offenders with a non-SUD psychiatric condition (major depressive disorder, etc.) and subsequently examined the presence of co-occurring SUDs (e.g., Abram, Teplin, & McClelland, 2003; James & Glaze, 2006).

An equally important consideration when determining prevalence estimates is the classification system used to arrive at diagnostic determinations. Given the conceptual and empirical problems noted for the DSM-IV SUD criteria (e.g., Hasin et al., 2013; Hasin & Paykin, 1998; Martin, Chung, & Langenbucher, 2008), the fifth edition of the DSM (DSM-5; APA, 2013) included several changes to the conceptualization of SUD classification, including most notably, a shift from the traditional categorical approach to a dimensional approach. Specific changes to diagnostic criteria included the removal of the recurrent legal problems criterion (DSM-IV abuse Criterion 3), addition of a criterion representing craving, and collapsing the DSM-IV abuse and dependence criteria into a single unified SUD category of graded clinical severity, with a minimum of two criteria required to assign a diagnosis. SUD diagnoses now include three severity specifiers based on the number of positive criteria from a total of 11 possible symptoms; Mild (2-3 criteria); Moderate (4-5 criteria), and Severe (6+ criteria). Due to the substantive change in the conceptualization of SUD from the DSM-IV to the DSM-5, further work is warranted to provide timely SUD and co-occurring psychiatric condition prevalence estimates among inmates based on the classification system providers currently rely on for assigning diagnostic determinations (i.e., the DSM-5).

An additional limitation is that unlike annual nationally representative estimates for the noninstitutionalized U.S. general adult population, the latest large-scale studies investigating both SUD and psychiatric condition prevalence among correctional populations were conducted over a decade ago (e.g., James & Glaze, 2006; Karberg & James, 2005). As a result, not only are such prevalence estimates based on the prior diagnostic criteria (i.e., DSM-IV)—and are likely obsolete—but the lack of ongoing monitoring on an annual basis precludes an examination of potential changes in substance use and psychiatric trends over time among inmates. That is, large-scale, nationally representative estimates of a number of substance-specific SUDs and co-occurring psychiatric conditions are reported annually by way of SAMHSA’s National Survey of Drug Use and Health (NSDUH). An obvious strength of the NSDUH’s annual data collection efforts is that such methods allow for the opportunity to monitor and demonstrate potential changes or stability in the prevalence of certain disorders over time. Of particular interest, recent estimates among the noninstitutionalized U.S. general population revealed that while the SUD prevalence rates for alcohol, cannabis, and cocaine have evidenced significant decreases, the rates of opioid use disorders (i.e., both heroin and prescription pain relievers) have risen significantly for most age groups over the prior 10 years (SAMHSA, 2015).

The shortcoming pertaining to the potentially obsolete SUD prevalence data at the national or even local correctional level is also salient considering that the United States is currently in the midst of what many researchers and health care professionals have referred to as an “opioid epidemic,” with increases in opioid use, opioid use disorder, and opioid-related overdose mortality reported annually (Rudd, Seth, David, & Scholl, 2016; SAMHSA, 2015; Volkow, Frieden, Hyde, & Cha, 2014). Although the opioid epidemic is one of America’s foremost health crises and this phenomenon is well-documented at the general population level, it remains unclear if the reported SUD patterns are also occurring at the local correctional level. Another consideration is whether there may be localized differentials, such as whether rural versus urban or regional prevalence rates differ from national estimates. Finally, it is important to note that comparisons of prevalence rates over time between studies involving correctional populations are confounded by a number of methodological differences including assessment techniques, diagnostic criteria, correctional setting, and demographic compositions. Additional work investigating prevalence over time with research designs accounting for such issues is indicated.

The present study attempts to address the aforementioned limitations of previous research and fill the apparent gaps in the extant knowledge base by way of three aims. First, this study will compare comparable jail samples and determine whether various substance-specific SUDs and psychiatric conditions maintain consistent prevalence rates or vary over time. To accomplish this, recent data collected in 2016 from a sample of male jail inmates in a rural county will be compared with older data collected in 2008 from a comparable sample of male jail inmates in an adjacent rural county. Second, this study will determine whether the presentation of various conditions also vary over time—that is, do the syndromes (i.e., associated symptoms, or diagnostic criteria, that consistently occur together to characterize a diagnosable condition) for various conditions vary in terms of the internal consistency among the diagnostic symptoms or indications. Third, the feasibility for local jurisdictions to conduct periodic prevalence estimates of key conditions that overcome some of the methodological limitations involving use of simple screening will be determined.

Given that the 2008 data focused only on those male inmates who had a SUD, observed patterns in prevalence rates and internal consistency for the various conditions will be explored among only those male inmates with a DSM-IV diagnosis of substance dependence or a DSM-5 moderate to severe SUD diagnosis. Although the DSM-5 SUD criteria included several changes, previous research has demonstrated high rates of concordance between the DSM-IV diagnosis of dependence and the DSM-5 SUD diagnoses of moderate to severe SUD (i.e., those groups representing a more clinically severe condition) for various substances (Dawson, Goldstein, & Grant, 2013; Hasin et al., 2013; Kopak, Metze, & Hoffmann, 2014; Kopak, Proctor, & Hoffmann, 2012; Peer et al., 2013; Proctor, Kopak, & Hoffmann, 2012, 2014).

Although the conceptualization of SUD represented a marked shift from the DSM-IV to the DSM-5, the actual changes in the diagnostic criteria were relatively minimal and would not be expected to affect prevalence estimates for those with at least a DSM-5 moderate SUD diagnosis, as at least four criteria are required for such diagnoses. The primary area where changes in the diagnostic criteria for SUD might produce substantial differences is in diagnoses for “milder” conditions considering that previous research has found those with indications of alcohol dependence show good concordance with a DSM-5 moderate to severe alcohol use disorder diagnosis (Baley & Hoffmann, 2015). For example, in the case of individuals evaluated for driving under the influence (DUI), all cases would have qualified for at least an abuse diagnosis under the DSM-IV because only one criterion (recurrent use in situations in which it is physically hazardous such as driving an automobile) was required for an individual to receive an abuse diagnosis. With the DSM-5, however, at least two criteria are required resulting in some DUI offenders with a DSM-IV abuse diagnosis failing to receive any DSM-5 diagnosis.

As such, a comparison of DSM-IV substance-dependent inmates with DSM-5 moderate to severe SUD inmates in the current study allowed for the determination of whether the prevalence and characteristics (i.e., internal consistency) of the different conditions may have shifted over time in neighboring county jails. Both the current study and the previous study used a nearly identical structured diagnostic assessment interview (Comprehensive Addictions And Psychological Evaluation [CAAPE], Hoffmann, 2000; and the CAAPE-5, Hoffmann, 2013a) that was administered approximately 8 years apart to two county jail samples comparable in demographic composition and geographic region.

Method Data derived from structured clinical interviews of county jail inmates as part of two separate studies conducted 8 years apart were compared to accomplish the present study’s aims. Data collection for the first study (Study 1) concluded in 2008, and involved interviewing recently arrested county jail inmates who had screened positive on a brief addictions screen to determine whether they met DSM-IV criteria for substance dependence on one or more substances and identify co-occurring conditions. Data collection for the subsequent study (Study 2) concluded in 2016, and involved interviewing a random sample of inmates recently admitted within 1 to 4 days of booking to another jail in an adjacent county to Study 1 to determine DSM-5 SUD and co-occurring psychiatric condition prevalence rates. Both studies were approved by an appropriate Institutional Review Board. Participants A total of 176 male county jail inmates in Study 1 who had indications of a SUD were interviewed with the CAAPE (Hoffmann, 2000) to confirm that they met DSM-IV criteria for dependence on at least one substance and to identify co-occurring conditions. Study 1 was limited to male inmates because the detention center policy required that inmates had to be the same sex as the male clinical interviewer to use a secure private room on the housing unit to conduct the interview. As a result of this policy, analysis of the data from Study 2 was limited to male inmates. Further information regarding Study 1 methods and procedures is described in detail elsewhere (Proctor, Alvarez de la Campa, Medina-Reyes, & Hoffmann, 2017; Proctor & Hoffmann, 2012). In contrast, Study 2 was designed to estimate the local prevalence of behavioral health conditions via a random sample of inmates interviewed using the CAAPE-5 (Hoffmann, 2013a) within 24 to 96 hr of booking to another jail in an adjacent county. On the days the clinical psychology graduate student responsible for administering the interviews would be at the jail, a random sample was drawn from the available recently booked inmates. In terms of the demographic characteristics for the entire Study 2 sample (N = 200), 86.0% were White, 7.5% were American Indian, and 3.5% were Black. The average age for the entire Study 2 sample was 33.12 years (SD = 10.45). Approximately half worked either full-time (39.5%) or part-time (8.0%), and 44.0% were unemployed. Estimated annual income was low in that 68.0% reported earning less than US$20,000 in the year prior to incarceration. More than half (57.0%) had never been married, with only 16.0% reporting that they were currently married at the time of booking. The balance of the cases were divorced (18.5%), separated (7.5%), or widowed (1.0%). Demographic characteristics for the inmates in Study 1 and Study 2 with a DSM-IV/DSM-5 SUD are presented in Table 1 and stratified by study. The Study 1 sample (2008 data) consisted of 176 male substance-dependent inmates who were predominately White (73.9%) with an average age of 36.24 years (SD = 10.89). Almost half (48.9%) had never been married, and nearly as many (42.6%) had less than a high school education. Of the 200 male inmates interviewed in Study 2 (2016 data), 149 met DSM-5 criteria for at least one moderate or severe SUD. Overall, among those inmates in Study 2 with a DSM-5 moderate or severe SUD (n = 149), the demographics of the two samples were quite similar in that the vast majority (87.2%) of the inmates in Study 2 were White, which was consistent with the racial composition of the general population residing in the region in which both county jails were located (western North Carolina). However, the average age in Study 2 (31.74 years of age) was nearly 5 years younger, and fewer Study 2 inmates were employed either full- or part-time (43.6%) relative to Study 1 inmates (59.7%). Consistent with Study 1 inmates, educational attainment for Study 2 inmates was low with 38.3% not having graduated from high school, and 60.4% had never been married. Although there are some variations in select demographics, both samples were comprised primarily of White males with an average age in the early to mid 30s, who had never been married, and had relatively low educational attainment and high rates of unemployment. Table 1: Demographic Characteristics for Inmates With a Substance Use Disorder, Stratified by Study View larger version Measures Virtually identical clinical interviews were used in both studies (i.e., CAAPE and CAAPE-5). The two versions of the CAAPE instrument are largely identical with respect to SUD diagnoses except for the inclusion of an item on craving and consolidation of items on recurrent substance-related legal problems as a nondiagnostic piece of information for clinical planning purposes only. Items addressing compulsion to use and the other DSM-5 SUD constructs were already in the original version of the CAAPE, so changes to the CAAPE-5 were relatively minimal. Additional items were also added for posttraumatic stress disorder (PTSD) to account for the differences in DSM-5, but assessment of all other psychiatric conditions did not differ between the two versions of the CAAPE. Interviews in both studies were conducted by clinical psychology graduate students from the same local university with formal clinical interview training through coursework and external practicum placements. In addition, interviewers were required to complete a one-to-one training with the developer of the CAAPE/CAAPE-5 prior to administering the interview to any inmates for the purposes of the two studies. An advantage of the CAAPE-5 is that any qualified staff person or appropriately trained technician can administer the interview by following the detailed instructions for administration outlined in the manual (Hoffmann, 2013b). However, licensed professionals reviewing all available information including other records and personally interviewing individuals would be required for actual clinical practice and the assignment of diagnoses. Algorithms, or decision rules, can be used to indicate likely conditions for statistical analyses such as those involved in determining prevalence estimates. All Study 1 participants were administered the CAAPE to verify that all inmates met DSM-IV criteria for dependence on at least one substance. The CAAPE is a valid and reliable structured diagnostic assessment interview (e.g., Gallagher, Penn, Brooks, & Feldman, 2006; Proctor & Hoffmann, 2012; Tracy & Carkin, 2016) compatible with the DSM-IV, which assesses for indications of lifetime and current SUDs in addition to other prevalent psychiatric conditions. The SUD modules of the CAAPE assess for a number of substance classes, including alcohol, cannabis, cocaine, heroin and nonheroin opioids (e.g., prescription pain relievers), stimulants (i.e., amphetamines and methamphetamine), sedatives, hallucinogens, inhalants, and an “other” category for any additional substances reported. The CAAPE also includes a number of psychiatric subscales assessing for several mood, anxiety, trauma-related, and personality disorders. Study 2 had the primary objective of documenting local prevalence rates of SUDs and additional psychiatric conditions assessed by the CAAPE-5 among a random sample of county jail inmates using an updated version of the clinical interview instrument used in Study 1 modified for documenting conditions as defined by the DSM-5. Given that the CAAPE and CAAPE-5 included largely identical items, this facilitated comparison of the findings over time to determine the extent to which conditions formed consistent or cohesive syndrome profiles. Current DSM-5 and DSM-IV SUD diagnostic determinations were made from algorithms corresponding to the respective diagnostic formulations based on CAAPE-5/CAAPE items. The CAAPE-5 and CAAPE instruments include multiple items for each diagnostic criterion for all SUDs and most of the psychiatric conditions covered. Specifically, for the DSM-5 diagnoses, inmates were classified as having no SUD if they reported none or only one DSM-5 criterion in the 12-month period prior to incarceration, mild SUD if they met two to three criteria, moderate SUD if they met four to five criteria, and severe SUD if they met six or more criteria. Substance dependence diagnostic determinations in Study 1 were made in accordance with DSM-IV guidelines, which require a minimum of three positive responses out of a possible seven total criteria. Data Analyses The current study had two primary aims: (Aim 1) to compare the two samples in terms of which conditions’ prevalence rates remained stable and which showed variation over time, and (Aim 2) to determine whether the internal consistency of various conditions varied or remained constant. These latter analyses were conducted to determine whether the syndromes defined by their positive indications were stable even if the general prevalence varied over time. In addition, a secondary aim was to consider the feasibility of jurisdictions doing periodic evaluations of inmates to assess prevalence rates of key conditions. Because the original objectives of the two studies differed, the analyses for the current study required developing a subset of cases from Study 2. Study 1 selected only those with a DSM-IV substance dependence diagnosis for an evaluation of the impact of a journaling intervention on subsequent criminal recidivism. Study 2 had the objective of determining general prevalence of behavioral health conditions and determining whether some conditions were related to repeated offending. As prior work suggested that a diagnosis of dependence using the DSM-IV was relatively similar to a moderate to severe DSM-5 diagnosis, only the 149 cases with such a SUD diagnosis were extracted from Study 2 to produce a comparable sample for comparison with Study 1. The analyses for the current study consisted of comparing the prevalence rates of SUDs and co-occurring psychiatric conditions as well as the internal consistency estimates of the diagnostic indications for the conditions covered by the structured clinical interviews. Chi-square analyses were conducted to determine whether prevalence rates for substance-specific SUDs and co-occurring psychiatric conditions differed between study samples given an 8-year differential in data collection from jails located in adjacent counties. Individual item response sets that comprised the various diagnostic subscales were analyzed for internal consistency as indicated by Cronbach’s alpha. Internal consistency analyses explored whether the composition of the syndromes in terms of the diagnostic criteria remained the same or also shifted over time. The secondary aim of considering the practicality, or feasibility, of doing periodic routine estimates of prevalence rates was primarily a qualitative consideration of the experiences of the interviewers in the two studies and time documentation for completing the CAAPE-5 interviews.

Results General Prevalence Rates Prevalence rates were first examined for the full Study 2 sample (Table 2). With respect to the prevalence of SUDs, nearly 9 in every 10 inmates met criteria for any DSM-5 SUD in the total sample of 200 male inmates. Although the prevalence for any DSM-5 alcohol use disorder (mild, moderate, and severe) was quite high with approximately three-fourths of the sample meeting criteria, the most prevalent severe SUD involved stimulants (i.e., methamphetamine or amphetamines), followed by alcohol and opioids as the second and third most prevalent severe SUDs, respectively. The high overall prevalence for any alcohol use disorder appears to be accounted for by the high rate of mild alcohol use disorder (37.0%) relative to the rate of mild SUD for the other substances. In addition, 15.0% met criteria for a severe cannabis use disorder, and only 3.5% had a severe cocaine use disorder. Similar to the SUDs, psychiatric conditions were prevalent among the entire Study 2 sample. The most prevalent non-SUD psychiatric conditions included PTSD, antisocial personality disorder, and panic attacks with nearly half of the sample meeting criteria for these three conditions. Mood disturbances were also common as two in every five inmates reported a past-year major depressive episode, and nearly one-third experienced a manic episode. Table 2: Prevalence of DSM-5 Substance Use and Psychiatric Disorders Among Entire Sample (N = 200) View larger version Comparisons of Prevalence Over Time Observed prevalence rates for SUDs were first compared between Study 1 and the Study 2 subsample of inmates with at least one DSM-5 moderate to severe SUD diagnosis (Table 3). Considering that the Study 1 sample consisted of inmates with a DSM-IV substance dependence diagnosis, only those inmates in the Study 2 sample who met DSM-5 criteria for a moderate or severe SUD were included to allow for comparisons over time. Limiting the Study 2 sample to those with at least a moderate SUD provided for a more appropriate subsample for comparison. As can be seen in Table 3, Study 1 found alcohol to be the most prevalent SUD diagnosis among inmates with a substance dependence diagnosis, followed by cannabis and cocaine. SUDs related to opioids and stimulants were relatively infrequent in Study 1. In sharp contrast, Study 2 found stimulants to be the most prevalent SUD diagnosis among inmates with a moderate-severe SUD diagnosis, followed by alcohol and opioids as the second and third most prevalent SUDs, respectively. The prevalence of a cannabis use disorder diagnosis remained relatively constant, but similar to alcohol, cocaine evinced a dramatic drop from 2008 to 2016. Table 3: Comparison of SUD Prevalence: From 2008 to 2016 View larger version Unlike the SUDs, changes in the prevalence rates for co-occurring psychiatric conditions between 2008 and 2016 were far less notable (Table 4). In fact, most Study 2 prevalence rates for the various co-occurring psychiatric conditions were within about 1% to 7% points of the rates found in Study 1. Antisocial personality disorder, PTSD, and major depressive episodes were among the most prevalent co-occurring psychiatric conditions in both samples. Similarly, indications of possible psychoses and bipolar I disorder were the least prevalent in both samples. Table 4: Comparison of Co-Occurring Psychiatric Condition Prevalence: From 2008 to 2016 View larger version Internal Consistency Comparisons of internal consistency between studies revealed that Cronbach’s alphas were remarkably similar and consistently high for all SUD classes (Table 5). This suggests that the constructs that define SUDs—whether based on the DSM-IV or DSM-5—are highly correlated and define a very robust syndrome. When considering DSM-IV substance dependence and DSM-5 SUD, stimulant and opioid use disorders consistently demonstrated the highest internal consistency, while cannabis had the lowest in both studies. This suggests that the conditions associated with some substances may produce more pronounced or consistent syndromes relative to other substances. The internal consistency coefficients for the co-occurring psychiatric conditions also revealed general consistency both between the two studies over time as well as among the different psychiatric subscales (Table 5). Overall, the Cronbach’s alphas for seven of the 11 comparisons were virtually identical with a coefficient difference of .02 or less between the studies. The greatest differential, albeit still not dramatically different, was seen for antisocial personality disorder. Table 5: Comparison of Internal Consistency Estimates: From 2008 to 2016 View larger version Feasibility of Routine Prevalence Estimates The feasibility of jurisdictions to conduct periodic estimates of prevalence for behavioral health conditions depends upon two factors: willingness of inmates to participate in a diagnostic interview, and time and resources required to conduct such interviews. Regarding the former, the interviewers in both studies had no difficulty in recruiting participants selected for personal interviews. There is a caveat, based on prior research (Proctor, Hoffmann, & Corwin, 2011), in that the interviewer must present as a neutral individual conducting a confidential interview and not a sworn officer or identified jail authority figure. Specifically, previous research conducted with county jail inmates has shown that attempts at screening for SUDs by jail staff (correctional officers at classification) results in gross denial of problems. During Study 2, the interviewer timed the administration of the CAAPE-5 beginning with the first question until the answer for the last question. This ranged from 9 min for an inmate who denied any condition to up to 46 min for an inmate with extensive positive findings. The average time for the actual administration was 26 min and 37 s (SD = 6 min).

Discussion Regarding the primary aims, this study demonstrated that while some conditions may be more likely to vary over time, the underlying characteristics of the conditions, or syndromes, tend to remain constant. The secondary aim suggests that many, if not most, jurisdictions can feasibly conduct periodic estimates of prevalence for common behavioral health conditions. Such estimates can inform policy initiatives and priorities as they may influence how to best address behavioral health conditions and possible impacts on criminal recidivism in an efficient manner. The current study provides empirical documentation of the prevalence of various DSM-5 substance-specific SUDs and psychiatric conditions among a rural county jail population. In accord with previous work examining DSM-IV prevalence rates among correctional populations, both DSM-5 SUDs and psychiatric conditions were common among the sample of recently booked county jail inmates. Given that the current study utilized a virtually identical valid and reliable structured clinical assessment interview to formulate diagnostic determinations—coupled with a comparable county jail sample in terms of demographics and geographic region to that of a similar previous study—we were afforded with the unique opportunity to examine differences in prevalence rates over time to determine potential changes or stability over an 8-year period. Perhaps the most important implication of the comparison of prevalence rates over time from 2008 to 2016 is that estimates of different conditions may require regular monitoring for specific populations in question. That is, despite the use of similar methodology, comparable sample populations, and county jails in the same geographic region, there were marked differences between studies over time with respect to the prevalence of certain SUDs. Of particular interest, while findings from Study 1 were consistent with prior work which found alcohol dependence to be the more prevalent SUD among inmates (Gunter et al., 2008; Jones & Hoffmann, 2006; Karberg & James, 2005; Peters et al., 1998), more recent data from Study 2 demonstrated that the prevalence of moderate-severe SUDs related to stimulants (i.e., methamphetamine and amphetamines) surpassed alcohol as the most common. In fact, 8 times more inmates with a SUD met DSM-5 criteria for a moderate or severe stimulant use disorder in 2016 relative to inmates who met DSM-IV criteria for stimulant dependence in 2008. Study 2 SUD prevalence estimates also revealed that the proportion of inmates with a moderate-severe opioid use disorder in 2016 was twice that of the prevalence of dependence found in Study 1 from 2008. These findings are fairly consistent with local societal changes in the use of particular substances in the counties affected. However, findings only partially reflect the patterns reported from national data of the U.S. general adult, noninstitutionalized population, which have found shifts in prevalence of both use and SUDs over time, but only for opioids (i.e., heroin and prescription pain relievers; SAMHSA, 2015) and not stimulants. That is, the current study evidenced an especially high prevalence of stimulant use disorder, as well as a dramatic increase in prevalence over time, which is inconsistent with national data showing fairly comparable rates of stimulant (i.e., prescription amphetamines and methamphetamine) use since 2002 (SAMHSA, 2015). However, it is important to note that previous annual estimates derived from the National Survey on Drug Use and Health are likely to underestimate the true prevalence of methamphetamine use in the general population over time given that prior to 2015, questions about methamphetamine use were asked in the context of questions about misuse of prescription stimulants only and failed to accurately capture the extent of illicit methamphetamine use. The increased use of stimulants evidenced in the current study at the local level may be unique to the largely rural geographic region studied, the county jail inmate population in particular, or a combination of the two. Anecdotally, discussions with the sheriff and other local law enforcement officials from the county in which the Study 2 jail facility was located confirmed that they had recognized a noticeable shift in arrests involving stimulants, along with noting a greater emphasis being placed on locating methamphetamine laboratories (i.e., “meth labs”) in the more rural areas of the county. Thus, it is possible that the observed changes in prevalence for SUDs related to stimulants may be attributed—at least in part—to the crime prevention strategy of local law enforcement agencies to prioritize crimes involving methamphetamine. However, it is also conceivable that the increased use of stimulants in recent years in the county may be responsible for the increased emphasis on methamphetamine-related crimes. Regardless of the potential cause(s) for the observed increases, the relatively high rate of stimulant use evidenced in the current study is noteworthy and warrants further replication work with additional populations to confirm the preliminary findings reported here. If future investigations substantiate the observed findings and contribute to this new knowledge base regarding the pervasive problem of increased stimulant use and related problems, development and testing of appropriate prevention and intervention strategies to curtail problematic stimulant use should follow. The findings regarding the high general prevalence rate of opioid use disorders in Study 2, and the increased rate of SUD diagnoses involving opioids from 2008 to 2016 is consistent with America’s current “opioid epidemic” and the patterns at the national level, which have documented annual increases in the rates of opioid use, opioid use disorders (i.e., both heroin and prescription pain relievers), and opioid-related overdose deaths in recent years (Rudd et al., 2016; SAMHSA, 2015; Volkow et al., 2014). With respect to the finding concerning an increased rate of opioid use disorders, the perceptions of local law enforcement officers included the observation that more individuals appeared to be involved with prescription pain reliever medications, and that some individuals reported transitioning to heroin because it could be obtained more easily and cheaply than the nonheroin opioids; an observation supported by previous research (Inciardi, Surratt, Cicero, & Beard, 2009; Mars, Bourgois, Karandinos, Montero, & Ciccarone, 2014; Siegal, Carlson, Kenne, & Swora, 2003). Interestingly, unlike the observed shifts in SUD prevalence rates, the 2016 data regarding the prevalence rates of all studied co-occurring psychiatric conditions in the Study 2 subsample were comparable with those found in the previous study in 2008. Even among one of the largest differentials, the prevalence of a co-occurring antisocial personality disorder in 2016 was still within about 7% points of its prevalence 8 years prior. Consistent with earlier work (James & Glaze, 2006; Staton-Tindall et al., 2015), major depressive episodes and PTSD were among the most prevalent co-occurring conditions in 2008 and remained so even 8 years later. These findings suggest that in sharp contrast to substance-specific SUDs, co-occurring psychiatric conditions appear to be more consistent with relatively stable prevalence rates over time. The current study also explored whether characteristics of the conditions, such as the internal consistency of the different conditions, were consistent both within a given diagnosis and between samples over time. The internal consistency estimates for the various substance-specific DSM-5 SUDs assessed by the CAAPE-5 in Study 2 were all well above the acceptable range (Nunnally, 1978). In fact, the CAAPE-5 yielded Cronbach’s alphas above .93 for four of the five substance-specific DSM-5 SUDs, indicative of a high level of internal consistency. The Cronbach’s alphas for the co-occurring psychiatric conditions in Study 2, although all still within the acceptable range, were somewhat more variable relative to the SUDs. Internal consistency of the items covering a given condition provide an indication of how robust or stable the characteristics of the condition are. These internal consistency findings indicate that the constructs that define the various psychiatric conditions are consistent in their interrelationships within a diagnosis irrespective of the specific diagnostic formulation employed (DSM-IV vs. DSM-5) or the relative prevalence of the conditions. Similar to the prevalence findings regarding the co-occurring psychiatric conditions, internal consistency estimates also remained relatively stable over time for nearly all of the psychiatric conditions with few differences in Cronbach’s alphas over the span of the 8-year period. As noted earlier, comparison of internal consistency over time also revealed generally stable estimates between the two studies. For example, Cronbach’s alphas for conditions such as major depressive and manic episodes were virtually identical from 2008 to 2016. The greatest differential, albeit not dramatically different, was seen for antisocial personality disorder. For this condition, consideration of the items for conduct disorder as an adolescent (which is a prerequisite for the condition) were combined with the adult behaviors for antisocial personality disorder. The lack of consistency over time might be due to the large number of items and the likely substantial variation in how one might meet the criteria for conduct disorder. Finally, the experiences with these two studies suggest that it would be feasible for most jurisdictions to conduct periodic estimates of behavioral health conditions. So long as the interview is presented as a confidential exploration of common conditions by an impartial individual not identified as a correctional officer (medical staff, clinical trainees, etc.), cooperation from inmates can be expected (Proctor et al., 2011). Based on the interview administration time estimates from Study 2, a time commitment for the interviewer to collect diagnostic data would amount to under 30 min of effort for each individual in the sample. However, in practice, one would have to allow for additional time to select, recruit, and orient the individual to the interview—possibly doubling the time for the interview itself to approximately 1 hr. The time commitment for data collection would seem feasible for most jurisdictions to periodically collect data on 100 or more randomly selected inmates for estimating local prevalence. Analyses could be conducted via Microsoft Excel spreadsheets or a commonly available statistical software package. Data collection and analysis could also be completed as collaborative efforts by faculty or graduate students affiliated with local universities or colleges in the surrounding area with access to appropriate programs and statistical packages. Producing a report for the correctional facility could even be incorporated into independent graduate student research projects (e.g., directed research courses, master’s theses, doctoral dissertations). In addition, it is important to note that the CAAPE-5 has branching or decision rules so as to reduce time spent on areas that will not yield diagnostic findings. For example, for those who deny a period of depression, a panic attack, or serious traumatic experience, skip functions allow the interviewer to omit asking the remaining items for major depression, panic attacks, or PTSD. Other than antisocial personality disorder, the questions regarding other personality disorders and psychosis could be skipped in the context of routine jail screening due to low base rates, thus shortening the interview time. Another potential time and cost savings option would be for a jurisdiction to select conditions from public domain interview instruments and limit the inquiry only to those key conditions of interest. In any event, it appears realistic for a jurisdiction to conduct periodic estimates of condition prevalence. Limitations The current findings should be considered in light of several limitations. First, both study samples consisted only of male county jail inmates. As such, the implications for female inmates and noncorrectional populations cannot be determined. Second, both study samples were predominately White, which does not represent the demographics of many urban jails or those with a more varied racial composition. Third, the prevalence and internal consistency estimates were derived from essentially one clinical diagnostic assessment instrument and were not verified by expert clinical determinations. Fourth, although the two county jails serve similar jurisdictions, the data were not derived from the same facility, thereby raising the question as to whether geographic differentials may have played some part in the observed prevalence differences. Another possible limitation would be whether either jurisdiction implemented any new policies surrounding arrests and detention that may account for the observed differences. However, to the authors’ knowledge, neither jurisdiction implemented any systematic diversion efforts prior to data collection for either study that would alter the makeup of recent arrestees. Only a prearrest diversion program would be capable of altering the clinical profile of those being booked, and such a program would have had to differentially affect SUD prevalences as opposed to other psychiatric conditions given the reported findings. Methodological differences between studies may also account for some of the observed differences in prevalence for certain disorders over time. Although both study samples were comprised exclusively of male inmates, a male graduate student interviewer was used in Study 1 while a female graduate student administered all interviews in Study 2. Finally, although adequate for a preliminary evaluation, the sample sizes used for the comparisons over time were relatively small. As such, some caution is warranted in generalizing the findings to other jail populations—even in other rural areas. Conclusion Despite the study limitations, one can assert three important conclusions. First, prevalence estimates for SUDs must be kept relevant by way of periodic assessment and monitoring given that use and related diagnosable conditions are likely to fluctuate over time. Second, the co-occurring psychiatric conditions defined by a single set of criteria for each given condition appear to have remained relatively stable, which suggests that the conditions present in a more consistent manner over time. The third is that most jurisdictions should be able to conduct periodic estimates of prevalence rates for the more common conditions presenting in local jail settings.

Norman G. Hoffmann is the author and copyright holder for the CAAPE-5 (Comprehensive Addictions And Psychological Evaluation–5) and receives royalties on the sale of the instrument.