PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Addict Med. Author manuscript; available in PMC 2014 January 1.
Published in final edited form as:
PMCID: PMC3634916
NIHMSID: NIHMS429700

Demographic and Clinical Characteristics of Middle-Aged versus Younger Adults Enrolled in a Clinical Trial of a Web-Delivered Psychosocial Treatment for Substance Use Disorders

Abstract

Objective

Evidence suggests that substance abuse is becoming more prevalent in middle-aged adults. The objective of this secondary analysis was to add to the growing empirical literature on the unique features of middle-aged substance abuse populations.

Methods

We descriptively compared baseline demographic and clinical characteristics of middle-aged (age 45–62, n = 111) and younger (age 18–44, n = 395) substance abusers entering a web-based psychosocial treatment study as part of the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN).

Results

A significantly greater percentage of middle-aged adults were non-Caucasian and had a marital status other than single/never married. There was a significant association between frequency of Internet use and the age group. Forty-six percent of middle-aged adults versus 21% of younger adults reported no Internet use in the prior 90 days. A significantly greater percentage of middle-aged adults used cocaine, and a significantly greater percentage of younger adults used marijuana and opioids. Clinically significant cognitive impairment (z less than −1.0) was found for the average participant in both groups on logical association of familiar concepts.

Conclusions

This secondary analysis of a NIDA CTN study provides additional information on the unique features of middle-aged substance abusers. Increasing knowledge of similarities and differences between younger and middle-aged substance abusers can help with potential age-specific substance abuse treatment planning.

Keywords: aging, web-based treatment, addiction, neurocognitive

Introduction

Evidence suggests that substance abuse is becoming more prevalent in the middle-aged adult population (Arndt et al., 2011; Blazer and Wu, 2011; Dinitto and Choi, 2010; Lofwall et al., 2008; SAMHSA, 2011; Satre et al., 2011), typically defined as age 45 and older in the substance abuse literature (Cicero et al., 2012; Hasin et al., 2005; Rabinowitz et al., 1996). As adults grow older, they begin to have unique neurocognitive (Hulette et al., 1998; Kalapatapu et al., 2011), biological (Ersche et al., 2012; Lin et al., 2011), psychological (Han et al., 2011; Miller, 1991) and social (Fiori et al., 2006; Gordon et al., 2012; Kuerbis and Sacco, 2012; Nielsen et al., 2010) characteristics that impact substance abuse treatment outcomes. The literature on such characteristics has emerged in recent years (Dowling et al., 2008; Frances, 2011), which may potentially help develop substance abuse treatments specific to middle-aged adults (Crome et al., 2011; Rothrauff et al., 2011). For example, in recent years, middle-aged adults are increasingly endorsing cocaine as their primary drug of choice (Arndt et al., 2011). Compared to younger patients, middle-aged patients on opioid maintenance have been found to have increased medical morbidity (e.g., cardiovascular, gastrointestinal and bone/joint problems) (Lofwall et al., 2005).

The objective of this secondary analysis was to add to such growing empirical literature on the unique features of middle-aged substance abuse populations. Specifically, we descriptively compared baseline demographic and clinical characteristics of middle-aged and younger substance abusers entering a web-based psychosocial treatment study (Bickel et al., 2008) as part of the National Institute on Drug Abuse (NIDA) Clinical Trials Network (CTN) (Campbell et al., 2012). The study has broad inclusion and few exclusion criteria and is intended to draw a quasi-representative sample of patients seeking treatment for drug or alcohol problems in community-based treatment programs in the United States. This study also includes a novel web-based platform for treatment delivery that allows for comparing technology use between middle-aged and younger substance abusers. Based on the chronicity and morbidity of substance abuse in middle-aged populations (Cicero et al., 2012; Lofwall et al., 2005; Patterson and Jeste, 1999), we hypothesized that middle-aged adults would have overall poorer psychiatric, medical and age and education-adjusted neurocognitive characteristics at baseline compared to younger adults.

Methods

Study Design, Setting and Measures

Full details about the study’s design, recruitment settings and measures can be found in a prior publication (Campbell et al., 2012). Briefly, this study is evaluating a web-based version (Bickel et al., 2008) of the Community Reinforcement Approach (Hunt and Azrin, 1973), in addition to prize-based contingency management (Budney et al., 1991), among participants enrolled in 10 outpatient substance abuse treatment programs located across the United States. Eligible participants were in the first 30 days of the current treatment episode, reported using an illicit substance within the 30 days prior to screening, and not receiving opioid replacement medications. 1,781 participants were screened, 848 were ineligible (47.6%), 426 (23.9%) were eligible but did not enroll in the study, and 507 (28.5%) were eventually randomized. The primary reasons for ineligibility were: no recent illicit substance use (83.5%); not planning a treatment episode of at least 90 days (11.8%); in the current treatment episode greater than 30 days (8.5%); and currently prescribed an opioid replacement therapy (7.9%). One participant aged 67 was excluded for this analysis due to being over age 65. Final data collection was concluded in June 2012; this analysis used data as of September 2012, which included all baseline information.

Demographic variables included sex, age, race/ethncity, education, marital status, and employment status. Frequency of Internet use was a self-report categorical measure, asking participants how often they accessed the Internet in the past 90 days.

Probable current diagnoses across six psychiatric disorders were assessed using the Patient Health Questionnaire (PHQ) (Spitzer et al., 1999) and included major depression, attention-deficit/hyperactivity disorder (ADHD), posttraumatic stress disorder (PTSD), and panic, social anxiety, and generalized anxiety disorders (combined the last three into one category). The Brief Symptom Inventory-18 (Derogatis, 2000) (total global score) measured overall psychological distress level. Days of substance use were collected using the Timeline Follow Back method (Sobell et al., 1979). Age of onset for first substance use dependence and primary substance of abuse were both collected using the DSM-IV symptom questionnaire (Hudziak et al., 1993). The Coping Strategies Scale (Litt et al., 2003) (total number of endorsed strategies of 23 where endorsed equals occasionally or frequently; range 0–23) assessed change processes used in altering substance use behavior.

The Euro Quality of Life (Qol) questionnaire assessed participants’ perception of their physical health using a visual analogue scale (range 0–100; where 100 is the best health) (1990). Medical service utilization was measured for the 90 days prior to baseline and included number of doctor visits, emergency department visits, and hospital admissions.

The MicroCog computerized Assessment of Cognitive Functioning has been normalized and standardized for adults (Powell et al., 1993). Based on our previous work (Aharonovich et al., 2008; Aharonovich et al., 2006), a custom version of the MicroCog (20–25 minutes in length) was used to measure working memory (Numbers Forward and Reversed), immediate/delayed memory (Wordlist 1 and 2), logical association of familiar concepts (Analogies), and spatial recognition/logic (Object Match A and B; Clocks). Subtest raw scores were transformed to scaled scores (μ = 10.0, σ = 3.0) and descriptively defined (Powell et al., 1993) as: μ ≤ 4 (below average), 8 > μ > 4 (low average), 13 > μ ≥ 8 (average), and μ ≥ 13 (above average). Scaled scores were also transformed to standard scores (z); we used a cut-off of z less than −1.0 (Jak et al., 2009) to define clinically significant cognitive impairment, which is frequently used in neuropsychological studies across diverse clinical populations (Bischkopf et al., 2002; Busse et al., 2006; Karzmark et al., 2012; Lonie et al., 2008; Patti et al., 2009; Reichenberg et al., 2009).

Statistical Analysis

A cut-off of age 45 was used to divide the sample into younger and middle-aged groups. Either the χ2 test or the Kruskal-Wallis test (non-parametric equivalent of the one-way independent ANOVA) was used to compare both groups across variables. A p-value of less than 0.05 was considered significant. All analyses were conducted using SAS version 9.3 (Cary, NC).

Results

Demographic Data

Table 1 presents baseline demographic features of the randomized sample. The median age of the entire sample was 33. The mean age of the younger group was 30.38 (S.D. = 7.36) and 50.66 (S.D. = 4.30) for the middle-aged group. Of the 111 participants in the middle-aged group, twenty two were 55 or older. A significantly greater percentage of middle-aged adults were non-Caucasian and had a marital status other than single/never married. There was a significant association between frequency of Internet use and the age group. Forty-six percent of middle-aged adults versus 21% of younger adults reported no Internet use in the prior 90 days.

Table 1
Middle-aged adults versus Younger adults – Baseline demographic data.

Clinical Data

Table 2 presents baseline clinical features of the randomized sample. The number of days used of any substance in the past 90 days and the percentage of participants using alcohol, amphetamines and other drugs were similar between both groups. Middle-aged adults had a significantly later age of onset and greater duration of any substance dependence. A significantly greater percentage of middle-aged adults used cocaine, and a significantly greater percentage of younger adults used marijuana and opioids. On all other psychiatric and medical/physical health measures (e.g., coping strategies, quality of life, medical visits), both groups were similar.

Table 2
Middle-aged adults versus Younger adults – Baseline clinical data.

Neurocognitive Data

Table 3 presents baseline neurocognitive data of the scaled scores on the MicroCog subtests. Scores were age and education-adjusted. Compared to the younger substance abusing adults, middle-aged substance abusing adults performed statistically significantly lower on working memory (Numbers Forward and Reversed – low average), delayed memory (Wordlist 2 – average), and on one measure of spatial recognition (Object Match B – low average). Younger adults performed statistically significantly lower on another measure of spatial recognition (Object Match A – average). Both age groups performed statistically similarly on immediate memory (Wordlist 1 – low average), logical association of familiar concepts (Analogies – low average) and spatial logic (Clocks – average). Clinically significant cognitive impairment (z less than −1.0) was found for the average participant in both groups on logical association of familiar concepts (Analogies).

Table 3
Middle-aged adults versus Younger adults – Baseline age/education-adjusted neurocognitive data.

Discussion

As expected, middle-aged substance abusing adults significantly differed from younger substance abusing adults on several measures. However, it is equally important to note similarities across several other measures.

A greater percentage of middle-aged adults identified as non-Caucasian (59% vs. 44%). Since substance abuse can be underdiagnosed in Hispanics (Andrews, 2008) and African-Americans (Cully et al., 2005) as they grow older, our findings reinforce the need for clinicians to screen for substance abuse in non-Caucasian middle-aged adults.

The similarity between middle-aged and younger adults in the days used of any substance highlights the role of clinicians to continue screening for substance abuse as younger adults grow older, rather than assuming that younger adults will “mature out” of substance use over time (Snow, 1973; Winick, 1962). This finding is similar to our previous dataset in cocaine users (Kalapatapu et al., 2011), where we found a similar level (p > 0.05) of cocaine use between middle-aged adults (20.4 days of use in the past 30 days) and younger adults (23.45 days of use in the past 30 days).

The later age of onset of substance dependence in middle-aged adults compared to younger adults in this analysis is also consistent with our previous dataset (Kalapatapu et al., 2011) and other studies (Al-Otaiba et al., 2012; Arndt et al., 2002). This difference may be due to biased recall, where younger adults were more likely to remember an earlier age of onset because it is more recent due to their younger age. As these younger adults grow older, they may have more substance dependence chronicity and functional impairment because of an earlier age of onset of substance dependence.

The percentage of middle-aged adults reporting cocaine as their primary drug of choice was even greater than alcohol, which is reported as the most commonly abused substance in adults as they grow older (Choi and Dinitto, 2011). Trend data from the Treatment Episode Dataset (TEDS) show that middle-aged adults endorsing cocaine as their primary drug of choice is on the rise (Arndt et al., 2011). Studies in emergency room settings (Chait et al., 2010; Rivers et al., 2004; Schlaerth et al., 2004) show that cocaine is used more among adults as they grow older than normally assumed. So, the data in this analysis are consistent with a growing trend of cocaine abuse in middle-aged adults.

It is not unexpected that a greater percentage of middle-aged adults had a marital status other than single/never married, since living longer might lead to more opportunities to have a change in one’s marital status. Also, it is not unexpected that much of the middle-aged group was between the ages of 45–55 compared to age 55+; this distribution is consistent with national samples, such as the Treatment Episode Data Set (TEDS, 2010).

Surprisingly, we found no differences in psychiatric comorbidity between age groups. Depression has been reported to be prevalent among substance abuse populations as they grow older (Blixen et al., 1997; Blow et al., 2004; Schonfeld et al., 2009), but the present analysis shows no difference in current depressive disorders between age groups. Similarly surprising was the lack of increased medical comorbidity in the middle-aged group, also a common finding in middle-aged substance abuse populations (Lofwall et al., 2005). Mood and anxiety disorders are often underdiagnosed in adults as they grow older (Gum et al., 2009; Sable and Jeste, 2001; VanItallie, 2005). The lack of increased psychiatric or medical comorbidity in this sample of middle-aged adults may be due to the self-report format of the psychiatric and medical measures used, as opposed to a standardized clinical psychiatric interview (e.g., Structured Clinical Interview for DSM-IV-TR Disorders). Furthermore, collateral medical history from a family member or primary care physician was not obtained. These findings, however, highlight the high rates of comorbidity overall among this treatment-seeking sample.

Regarding coping strategies, there is some evidence that more reliance on avoidance coping strategies can predict late-life problem drinking (Schutte et al., 1998) and that neurocognitive ability (e.g., processing speed) can moderate the relationship between coping strategies (e.g., self-blaming) and drinking outcomes, such as subsequent drinking (Tapert et al., 2004). We found no difference in coping strategies between the middle-aged and younger groups at baseline. The impact of coping strategies on substance abuse outcomes will be reported in a future manuscript.

The neurocognitive data show that assessing and recognizing the role of cognitive functioning in substance abuse treatment planning for middle-aged adults is important. This is consistent with earlier findings that adults are more likely to be cognitively sensitive to substances as they grow older (Dowling et al., 2008; Fingerhood, 2000). Due to brain abnormalities present in a middle-aged substance abuser that are not found in a healthy aging brain (Bartzokis et al., 2002; Bartzokis et al., 1999), cognitive impairment in an middle-aged substance abuser may be above and beyond the normal cognitive changes (e.g., working memory) expected for a middle-aged adult. If clinically significant cognitive impairment is found, psychotherapeutic interventions may need to be modified (Aharonovich et al., 2008; Aharonovich et al., 2006) or cognitive rehabilitation techniques (Ball et al., 2002) may need to be added to the treatment plan to rehabilitate a cognitive domain. Compared to the younger group, the middle-aged group reported a later age of onset but greater duration of substance dependence. Thus, the associations between age and cognitive impairment could reflect adverse effects on the brain of longer exposure to substances, or cognitive impairment could be a premorbid factor that conveys risk for a more chronic course of substance dependence (Latvala et al., 2009; Newton et al., 2011; Tarter et al., 2003).

Of note in the neurocognitive data is the finding that the younger group performed worse than the middle-aged group on one measure of spatial recognition (Object Match A Total Score). This may be an aberrancy, or this may represent some difference in the dorsal stream of the visual processing pathway [involved in the processing of visuospatial information (Kravitz et al., 2011)] between both groups. This is speculative given the absence of neuroimaging in this study to better understand underlying neural correlates.

Regarding Internet use, adults are more likely to use the Internet when they are younger (Berner et al., 2012; Cohall et al., 2011). However, a growing literature shows adults are willing to use web-based platforms for general healthcare (Lukazewski et al., 2012; Robertson-Lang et al., 2011; Xie, 2011) and substance abuse treatment (Finfgeld-Connett, 2009; Postel et al., 2011) as they grow older. Web-based and mobile phone platforms represent a potentially innovative way to reach and treat middle-aged substance abusers who might suffer from social isolation, a common concern as adults grow older (Dickens et al., 2011).

This analysis has several strengths, such as a large sample size, a wide variety of outpatient clinical recruitment sites across the United States, and the assessment of baseline Internet use. There are also clear limitations. First, since participants were selected from outpatient programs, these findings may not be generalizable to other populations, such as inpatient or correctional populations. Further, since eligibility criteria required recent (past 30 days) illicit substance use, participants with alcohol use disorders only were not included. Second, our assessment of medical and psychiatric morbidity was limited, only relying on self-report. A more comprehensive medical and psychiatric assessment with collateral history from participants’ families or their primary care physicians might have yielded different findings. Third, we dichotomized the continuous variable of age, which may contribute to a loss of information about individual differences, as well as power (Altman and Royston, 2006; MacCallum et al., 2002; Naggara et al., 2011). Fourth, we don’t know if the neurocognitive data reflect premorbid capacity or substance-induced cognitive changes, which could have been further teased out with collateral history. Finally, the results could be biased due to the limitations of the study sample.

Conclusions

In summary, this secondary analysis of a NIDA CTN study provides additional information on the unique features of middle-aged substance abusers. Increasing knowledge of similarities and differences between younger and middle-aged substance abusers can help with potential age-specific substance abuse treatment planning.

Acknowledgments

Source of Funding:

The National Institute on Drug Abuse (NIDA) contributed to the development of study design and initial protocol. The EMMES Corporation, a subcontract vendor of NIDA, contributed to data management and quality assurance. Analysis, interpretation, manuscript preparation, and decision to submit the manuscript for publication was the sole responsibility of the authors. The study in this manuscript was funded by the NIDA grant 3U10DA013035-10S2 (PI: Nunes). Dr. Kalapatapu was funded by the NIDA grant 5T32DA007294-19 (PI: Levin) while he was at Columbia University and is now funded by a Clinical/Research Fellowship in Psychiatry through the Veterans Affairs Medical Center. Dr. Aharonovich was funded by the NIDA grant R01DA020647 (PI: Aharonovich). Dr. Levin was funded by the NIDA grant 5K24DA029647-02 (PI: Levin). Dr. Nunes was funded by the NIDA grant 2K24DA022412-06 (PI: Nunes).

Footnotes

Conflicts of Interest:

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the manuscript.

Contributors: Dr. Kalapatapu completed the background literature search. Dr. Hu completed the statistical analyses. Dr. Kalapatapu wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript.

References

  • Aharonovich E, Hasin DS, Brooks AC, Liu X, Bisaga A, Nunes EV. Cognitive deficits predict low treatment retention in cocaine dependent patients. Drug Alcohol Depend. 2006;81(3):313–322. [PubMed]
  • Aharonovich E, Amrhein PC, Bisaga A, Nunes EV, Hasin DS. Cognition, commitment language, and behavioral change among cocaine-dependent patients. Psychol Addict Behav. 2008;22(4):557–562. [PMC free article] [PubMed]
  • Al-Otaiba Z, Epstein EE, McCrady B, Cook S. Age-based differences in treatment outcome among alcohol-dependent women. Psychol Addict Behav. 2012 Feb 27; [Epub ahead of print] [PMC free article] [PubMed]
  • Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006;332(7549):1080. [PMC free article] [PubMed]
  • Andrews C. An exploratory study of substance abuse among Latino older adults. J Gerontol Soc Work. 2008;51(1–2):87–108. [PubMed]
  • Arndt S, Turvey CL, Flaum M. Older offenders, substance abuse, and treatment. Am J Geriatr Psychiatry. 2002;10(6):733–739. [PubMed]
  • Arndt S, Clayton R, Schultz SK. Trends in substance abuse treatment 1998–2008: increasing older adult first-time admissions for illicit drugs. Am J Geriatr Psychiatry. 2011;19(8):704–711. [PubMed]
  • Ball K, Berch DB, Helmers KF, et al. Effects of cognitive training interventions with older adults: a randomized controlled trial. JAMA. 2002;288(18):2271–2281. [PMC free article] [PubMed]
  • Bartzokis G, Goldstein IB, Hance DB, et al. The incidence of T2-weighted MR imaging signal abnormalities in the brain of cocaine-dependent patients is age-related and region-specific. Am J Neuroradiol. 1999;20(9):1628–1635. [PubMed]
  • Bartzokis G, Beckson M, Lu PH, Edwards N, Bridge P, Mintz J. Brain maturation may be arrested in chronic cocaine addicts. Biol Psychiatry. 2002;51(8):605–611. [PubMed]
  • Berner J, Rennemark M, Jogreus C, Berglund J. Distribution of personality, individual characteristics and internet usage in Swedish older adults. Aging Ment Health. 2012;16(1):119–126. [PubMed]
  • Bickel WK, Marsch LA, Buchhalter AR, Badger GJ. Computerized behavior therapy for opioid-dependent outpatients: a randomized controlled trial. Exp Clin Psychopharmacol. 2008;16(2):132–143. [PMC free article] [PubMed]
  • Bischkopf J, Busse A, Angermeyer MC. Mild cognitive impairment--a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr Scand. 2002;106(6):403–414. [PubMed]
  • Blazer DG, Wu LT. The epidemiology of alcohol use disorders and subthreshold dependence in a middle-aged and elderly community sample. Am J Geriatr Psychiatry. 2011;19(8):685–694. [PMC free article] [PubMed]
  • Blixen CE, McDougall GJ, Suen LJ. Dual diagnosis in elders discharged from a psychiatric hospital. Int J Geriatr Psychiatry. 1997;12(3):307–313. [PubMed]
  • Blow FC, Brockmann LM, Barry KL. Role of alcohol in late-life suicide. Alcohol Clin Exp Res. 2004;28(5 Suppl):48S–56S. [PubMed]
  • Budney AJ, Higgins ST, Delaney DD, Kent L, Bickel WK. Contingent reinforcement of abstinence with individuals abusing cocaine and marijuana. J Appl Behav Anal. 1991;24(4):657–665. [PMC free article] [PubMed]
  • Busse A, Angermeyer MC, Riedel-Heller SG. Progression of mild cognitive impairment to dementia: a challenge to current thinking. Br J Psychiatry. 2006;189:399–404. [PubMed]
  • Campbell AN, Nunes EV, Miele GM, et al. Design and methodological considerations of an effectiveness trial of a computer-assisted intervention: an example from the NIDA Clinical Trials Network. Contemp Clin Trials. 2012;33(2):386–395. [PMC free article] [PubMed]
  • Chait R, Fahmy S, Caceres J. Cocaine abuse in older adults: an underscreened cohort. J Am Geriatr Soc. 2010;58(2):391–392. [PubMed]
  • Choi NG, Dinitto DM. Drinking, smoking, and psychological distress in middle and late Life. Aging Ment Health. 2011;15(6):720–731. [PubMed]
  • Cicero TJ, Surratt HL, Kurtz S, Ellis MS, Inciardi JA. Patterns of prescription opioid abuse and comorbidity in an aging treatment population. J Subst Abuse Treat. 2012;42(1):87–94. [PMC free article] [PubMed]
  • Cohall AT, Nye A, Moon-Howard J, et al. Computer use, internet access, and online health searching among Harlem adults. Am J Health Promot. 2011;25(5):325–333. [PubMed]
  • Crome IB, Crome P, Rao R. Addiction and ageing-awareness, assessment and action. Age Ageing. 2011;40(6):657–658. [PubMed]
  • Cully JA, Molinari VA, Snow AL, Burruss J, Kotrla KJ, Kunik ME. Utilization of emergency center services by older adults with a psychiatric diagnosis. Aging Ment Health. 2005;9(2):172–176. [PubMed]
  • Derogatis LR. BSI 18: Brief Symptom Inventory 18. Minneapolis: National Computer Systems, Inc; 2000.
  • Dickens AP, Richards SH, Greaves CJ, Campbell JL. Interventions targeting social isolation in older people: a systematic review. BMC Public Health. 2011;11:647. [PMC free article] [PubMed]
  • Dinitto DM, Choi NG. Marijuana use among older adults in the U.S.A: user characteristics, patterns of use, and implications for intervention. Int Psychogeriatr. 2011;23(5):732–741. [PubMed]
  • Dowling GJ, Weiss SR, Condon TP. Drugs of abuse and the aging brain. Neuropsychopharmacology. 2008;33(2):209–218. [PubMed]
  • Ersche KD, Jones PS, Williams GB, Robbins TW, Bullmore ET. Cocaine dependence: a fast-track for brain ageing? Mol Psychiatry. 2012 Apr 24; [Epub ahead of print] [PMC free article] [PubMed]
  • EuroQol Group. EuroQol--a new facility for the measurement of health-related quality of life. Health Policy. 1990;16(3):199–208. [PubMed]
  • Finfgeld-Connett D. Web-based treatment for rural women with alcohol problems: preliminary findings. Comput Inform Nurs. 2009;27(6):345–353. [PMC free article] [PubMed]
  • Fingerhood M. Substance abuse in older people. J Am Geriatr Soc. 2000;48(8):985–995. [PubMed]
  • Fiori KL, Antonucci TC, Cortina KS. Social network typologies and mental health among older adults. J Gerontol B Psychol Sci Soc Sci. 2006;61B(1):25–32. [PubMed]
  • Frances RJ. Geriatric addictions. Am J Geriatr Psychiatry. 2011;19(8):681–684. [PubMed]
  • Gordon RJ, Rosenheck RA, Zweig RA, Rotem IH. Health and social adjustment of homeless older adults with a mental illness. Psychiatr Serv. 2012 Apr 1; [Epub ahead of print] [PubMed]
  • Gum AM, King-Kallimanis B, Kohn R. Prevalence of mood, anxiety, and substance-abuse disorders for older Americans in the national comorbidity survey-replication. Am J Geriatr Psychiatry. 2009;17(9):769–781. [PubMed]
  • Han B, Gfroerer JC, Colpe LJ, Barker PR, Colliver JD. Serious psychological distress and mental health service use among community-dwelling older U.S. adults. Psychiatr Serv. 2011;62(3):291–298. [PubMed]
  • Hasin DS, Hatzenbueler M, Smith S, Grant BF. Co-occurring DSM-IV drug abuse in DSM-IV drug dependence: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Drug Alcohol Depend. 2005;80(1):117–123. [PubMed]
  • Hudziak JJ, Helzer JE, Wetzel MW, et al. The use of the DSM-III-R Checklist for initial diagnostic assessments. Compr Psychiatry. 1993;34(6):375–383. [PubMed]
  • Hulette CM, Welsh-Bohmer KA, Murray MG, Saunders AM, Mash DC, McIntyre LM. Neuropathological and neuropsychological changes in “normal” aging: evidence for preclinical Alzheimer disease in cognitively normal individuals. J Neuropathol Exp Neurol. 1998;57(12):1168–1174. [PubMed]
  • Hunt GM, Azrin NH. A community-reinforcement approach to alcoholism. Behav Res Ther. 1973;11(1):91–104. [PubMed]
  • Jak AJ, Bondi MW, Delano-Wood L, et al. Quantification of five neuropsychological approaches to defining mild cognitive impairment. Am J Geriatr Psychiatry. 2009;17(5):368–375. [PMC free article] [PubMed]
  • Kalapatapu RK, Vadhan NP, Rubin E, et al. A pilot study of neurocognitive function in older and younger cocaine abusers and controls. Am J Addict. 2011;20(3):228–239. [PMC free article] [PubMed]
  • Karzmark P, Zeifert PD, Bell-Stephens TE, Steinberg GK, Dorfman LJ. Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery. 2012;70(3):634–638. [PubMed]
  • Kravitz DJ, Saleem KS, Baker CI, Mishkin M. A new neural framework for visuospatial processing. Nat Rev Neurosci. 2011;12(4):217–230. [PMC free article] [PubMed]
  • Kuerbis A, Sacco P. The impact of retirement on the drinking patterns of older adults: a review. Addict Behav. 2012;37(5):587–595. [PubMed]
  • Latvala A, Castaneda AE, Perala J, et al. Cognitive functioning in substance abuse and dependence: a population-based study of young adults. Addiction. 2009;104(9):1558–1568. [PubMed]
  • Lin WC, Zhang J, Leung GY, Clark RE. Chronic physical conditions in older adults with mental illness and/or substance use disorders. J Am Geriatr Soc. 2011;59(10):1913–1921. [PubMed]
  • Litt MD, Kadden RM, Cooney NL, Kabela E. Coping skills and treatment outcomes in cognitive-behavioral and interactional group therapy for alcoholism. J Consult Clin Psychol. 2003;71(1):118–128. [PubMed]
  • Lofwall MR, Brooner RK, Bigelow GE, Kindbom K, Strain EC. Characteristics of older opioid maintenance patients. J Subst Abuse Treat. 2005;28(3):265–272. [PubMed]
  • Lofwall MR, Schuster A, Strain EC. Changing profile of abused substances by older persons entering treatment. J Nerv Ment Dis. 2008;196(12):898–905. [PMC free article] [PubMed]
  • Lonie JA, Herrmann LL, Donaghey CL, Ebmeier KP. Clinical referral patterns and cognitive profile in mild cognitive impairment. Br J Psychiatry. 2008;192(1):59–64. [PubMed]
  • Lukazewski A, Mikula B, Servi A, Martin B. Evaluation of a web-based tool in screening for medication-related problems in community-dwelling older adults. Consult Pharm. 2012;27(2):106–113. [PubMed]
  • MacCallum RC, Zhang S, Preacher KJ, Rucker DD. On the practice of dichotomization of quantitative variables. Psychol Methods. 2002;7(1):19–40. [PubMed]
  • Miller L. Predicting relapse and recovery in alcoholism and addiction: neuropsychology, personality, and cognitive style. J Subst Abuse Treat. 1991;8(4):277–291. [PubMed]
  • Naggara O, Raymond J, Guilbert F, Roy D, Weill A, Altman DG. Analysis by categorizing or dichotomizing continuous variables is inadvisable: an example from the natural history of unruptured aneurysms. AJNR Am J Neuroradiol. 2011;32(3):437–440. [PubMed]
  • Newton NC, O’Leary-Barrett M, Conrod PJ. Adolescent substance misuse: neurobiology and evidence-based interventions. Curr Top Behav Neurosci. 2011 Nov 5; [Epub ahead of print] [PubMed]
  • Nielsen B, Nielsen AS, Lolk A, Andersen K. Elderly alcoholics in outpatient treatment. Dan Med Bull. 2010;57(11):A4209. [PubMed]
  • Patterson TL, Jeste DV. The potential impact of the baby-boom generation on substance abuse among elderly persons. Psychiatr Serv. 1999;50(9):1184–1188. [PubMed]
  • Patti F, Amato MP, Trojano M, et al. Cognitive impairment and its relation with disease measures in mildly disabled patients with relapsing-remitting multiple sclerosis: baseline results from the Cognitive Impairment in Multiple Sclerosis (COGIMUS) study. Mult Scler. 2009;15(7):779–788. [PubMed]
  • Postel MG, de Haan HA, ter Huurne ED, van der Palen J, Becker ES, de Jong CA. Attrition in web-based treatment for problem drinkers. J Med Internet Res. 2011;13(4):e117. [PMC free article] [PubMed]
  • Powell DH, Kaplan EF, Whitla D, Weintraub S, Caitlin R, Funkenstein HH. MicroCog: Assessment of Cognitive Functioning Version 2.1. San Antonio: The Psychological Corporation; 1993.
  • Rabinowitz J, Mark M, Popper M, Slyuzberg M. Reported comorbidity of mental disorders with substance abuse among psychiatric inpatients in Israel. J Ment Health Adm. 1996;23(4):471–478. [PubMed]
  • Reichenberg A, Harvey PD, Bowie CR, et al. Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders. Schizophr Bull. 2009;35(5):1022–1029. [PMC free article] [PubMed]
  • Rivers E, Shirazi E, Aurora T, et al. Cocaine use in elder patients presenting to an inner-city emergency department. Acad Emerg Med. 2004;11(8):874–877. [PubMed]
  • Robertson-Lang L, Major S, Hemming H. An exploration of search patterns and credibility issues among older adults seeking online health information. Can Journal Aging. 2011;30(4):631–645. [PubMed]
  • Rothrauff TC, Abraham AJ, Bride BE, Roman PM. Substance abuse treatment for older adults in private centers. Subst Abus. 2011;32(1):7–15. [PMC free article] [PubMed]
  • Sable JA, Jeste DV. Anxiety disorders in older adults. Curr Psychiatry Rep. 2001;3(4):302–307. [PubMed]
  • SAMHSA, Center for Behavioral Health Statistics and Quality. The NSDUH Report: Illicit Drug Use among Older Adults. Rockville, MD: Sep 1, 2011. [Accessed on 11/11/2012]. Available at: http://oas.samhsa.gov/2k11/013/WEB_SR_013_HTML.pdf.
  • Satre DD, Sterling SA, Mackin RS, Weisner C. Patterns of alcohol and drug use among depressed older adults seeking outpatient psychiatric services. Am J Geriatr Psychiatry. 2011;19(8):695–703. [PMC free article] [PubMed]
  • Schlaerth KR, Splawn RG, Ong J, Smith SD. Change in the pattern of illegal drug use in an inner city population over 50: an observational study. J Addict Dis. 2004;23(2):95–107. [PubMed]
  • Schonfeld L, King-Kallimanis BL, Duchene DM, et al. Screening and brief intervention for substance misuse among older adults: the Florida BRITE project. Am J Public Health. 2009;99(7):1–7. [PubMed]
  • Schutte KK, Brennan PL, Moos RH. Predicting the development of late-life late-onset drinking problems: a 7-year prospective study. Alcohol Clin Exp Res. 1998;22(6):1349–1358. [PubMed]
  • Snow M. Maturing out of narcotic addiction in New York City. Int J Addict. 1973;8(6):921–938. [PubMed]
  • Sobell LC, Maisto SA, Sobell MB, Cooper AM. Reliability of alcohol abusers’ self-reports of drinking behavior. Behav Res Ther. 1979;17(2):157–160. [PubMed]
  • Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. JAMA. 1999;282(18):1737–1744. [PubMed]
  • Tapert SF, Ozyurt SS, Myers MG, Brown SA. Neurocognitive ability in adults coping with alcohol and drug relapse temptations. Am J Drug Alcohol Abuse. 2004;30(2):445–460. [PubMed]
  • Tarter RE, Kirisci L, Mezzich A, et al. Neurobehavioral disinhibition in childhood predicts early age at onset of substance use disorder. Am J Psychiatry. 2003;160(6):1078–1085. [PubMed]
  • TEDS (Treatment Episode Data Set): U.S. Dept. of Health and Human Services, Substance Abuse and Mental Health Services Administration, Office of Applied Studies. Prepared by Synectics for Management Decisions, Incorporated. ICPSR33261-v1. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [producer and distributor]; 2012. Jul 17, TREATMENT EPISODE DATA SET - ADMISSIONS (TEDS-A), 2010 [Computer file]
  • VanItallie TB. Subsyndromal depression in the elderly: underdiagnosed and undertreated. Metabolism. 2005;54(5 Suppl 1):39–44. [PubMed]
  • Winick C. Maturing out narcotic addiction. Bull Narc. 1962;14:1–7.
  • Xie B. Effects of an eHealth literacy intervention for older adults. J Med Internet Res. 2011;13(4):e90. [PMC free article] [PubMed]