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Health outcomes associated with patterns of substance use disorders among patients with type 2 diabetes and hypertension: Electronic health record findings

  • Md Tareq Ferdous Khan
    Correspondence
    Correspondence to: Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, 160 Panzeca Way, Cincinnati, OH 45267, USA.
    Affiliations
    Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    Department of Statistics, Jahangirnagar University, Dhaka, Bangladesh
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  • Daniel Lewis
    Affiliations
    Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
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  • David C. Kaelber
    Affiliations
    Department of Information Services, The MetroHealth System, Cleveland, OH, USA

    Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    The Center for Clinical Informatics Research and Education, The MetroHealth System, Cleveland, OH, USA
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  • T. John Winhusen
    Affiliations
    Center for Addiction Research, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA
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Published:November 25, 2022DOI:https://doi.org/10.1016/j.pcd.2022.11.006

      Highlights

      • Substance use disorder patterns were identified in patients with type 2 diabetes and hypertension.
      • Tobacco use disorder (TUD) was associated with adverse diabetes outcomes and death.
      • Alcohol use disorder with TUD vs. TUD was associated with a higher risk of death.
      • Polysubstance use disorder vs. TUD associated with CVA, diabetic neuropathy, MI, and death.

      Abstract

      Aims

      To identify substance use disorder (SUD) patterns and their association with T2DM health outcomes among patients with type 2 diabetes and hypertension.

      Methods

      We used latent class analysis on electronic health records from the MetroHealth System (Cleveland, Ohio) to obtain the target SUD groups: i) only tobacco (TUD), ii) tobacco and alcohol (TAUD), and iii) tobacco, alcohol, and at least one more substance (PSUD). A matching program with Mahalanobis distance within propensity score calipers created the matched control groups: no SUD (NSUD) for TUD and TUD for the other two SUD groups. The numbers of participants for the target-control groups were 8009 (TUD), 1672 (TAUD), and 642 (PSUD).

      Results

      TUD was significantly associated with T2DM complications. Compared to TUD, the TAUD group showed a significantly higher likelihood for all-cause mortality (adjusted odds ratio (aOR) = 1.46) but not for any of the T2DM complications. Compared to TUD, the PSUD group experienced a significantly higher risk for cerebrovascular accident (CVA) (aOR = 2.19), diabetic neuropathy (aOR = 1.76), myocardial infarction (MI) (aOR = 1.76), and all-cause mortality (aOR = 1.66).

      Conclusions

      The findings of increased risk associated with PSUDs may provide insights for better management of patients with T2DM and hypertension co-occurrence.

      Keywords

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      References

        • Wu L.T.
        • et al.
        Using electronic health record data for substance use screening, brief intervention, and referral to treatment among adults with type 2 diabetes: design of a national drug abuse treatment clinical trials network study.
        Conte Clin. Trials. 2016; 46: 30-38https://doi.org/10.1016/j.cct.2015.11.009
        • Winhusen T.
        • et al.
        Medical complications associated with substance use disorders in patients with type 2 diabetes and hypertension: electronic health record findings.
        Addiction. 2019; 114: 1462-1470https://doi.org/10.1111/add.14607
        • Crummy E.A.
        • et al.
        One is not enough: understanding and modeling polysubstance use.
        Front Neurosci. 2020; 14: 569https://doi.org/10.3389/fnins.2020.00569
        • Connor J.P.
        • et al.
        Polysubstance use: diagnostic challenges, patterns of use and health.
        Curr. Opin. Psychiatry. 2014; 27: 269-275https://doi.org/10.1097/YCO.0000000000000069
        • Halladay J.
        • et al.
        Patterns of substance use among adolescents: a systematic review.
        Drug Alcohol Depend. 2020; 108222https://doi.org/10.1016/j.drugalcdep.2020.108222
        • Kaelber D.C.
        • et al.
        Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data.
        J. Am. Med Inf. Assoc. 2012; 19: 965-972https://doi.org/10.1136/amiajnl-2011-000782
        • Pfefferle K.J.
        • et al.
        Validation study of a pooled electronic healthcare database: the effect of obesity on the revision rate of total knee arthroplasty.
        Eur. J. Orthop. Surg. Trauma. 2014; 24: 1625-1628https://doi.org/10.1007/s00590-014-1423-2
        • Collins L.M.
        • Lanza S.T.
        Latent Class and Latent Transition Analysis: with Applications in the Social, Behavioral, and Health Sciences. Vol. 718. John Wiley & Sons, 2009
        • Hagenaars J.A.
        • McCutcheon A.L.
        Applied Latent Class Analysis.
        Cambridge University Press, Cambridge; New York2002: 454 (xxii)
        • Nylund K.L.
        • Asparouhov T.
        • Muthén B.O.
        Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study.
        Struct. Equ. Model.: A Multidiscip. J. 2007; 14: 535-569https://doi.org/10.1080/10705510701575396
      1. R Core Team, R: A Language and Environment for Statistical Computing. 2020, R Foundation for Statistical Computing: Vienna, Austria.

        • Stuart E.A.
        Matching methods for causal inference: a review and a look forward.
        Stat. Sci. 2010; 25: 1-21https://doi.org/10.1214/09-STS313
        • Cade W.T.
        Diabetes-related microvascular and macrovascular diseases in the physical therapy setting.
        Phys. Ther. 2008; 88: 1322-1335https://doi.org/10.2522/ptj.20080008
        • Lee H.
        • et al.
        Substance use patterns among adolescents: a latent class analysis.
        J. Am. Psychiatr. Nurses Assoc. 2020; 26: 586-594https://doi.org/10.1177/1078390319858658
        • Evans B.E.
        • Kim Y.
        • Hagquist C.
        A latent class analysis of changes in adolescent substance use between 1988 and 2011 in Sweden: associations with sex and psychosomatic problems.
        Addiction. 2020; 115: 1932-1941https://doi.org/10.1111/add.15040
        • Lorvick J.
        • et al.
        Polydrug use patterns, risk behavior and unmet healthcare need in a community-based sample of women who use cocaine, heroin or methamphetamine.
        Addict. Behav. 2018; 85: 94-99https://doi.org/10.1016/j.addbeh.2018.05.013
        • Lanza S.T.
        • Rhoades B.L.
        Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment.
        Prev. Sci. 2013; 14: 157-168https://doi.org/10.1007/s11121-011-0201-1
        • Conti A.A.
        • et al.
        Chronic tobacco smoking and neuropsychological impairments: a systematic review and meta-analysis.
        Neurosci. Biobehav Rev. 2019; 96: 143-154https://doi.org/10.1016/j.neubiorev.2018.11.017
        • Musk A.W.
        • de Klerk N.H.
        History of tobacco and health.
        Respirology. 2003; 8: 286-290https://doi.org/10.1046/j.1440-1843.2003.00483.x
        • Saha S.P.
        • et al.
        Cigarette smoke and adverse health effects: an overview of research trends and future needs.
        Int. J. Angiol. 2007; 16: 77-83https://doi.org/10.1055/s-0031-1278254
      2. HHS, in The Health Consequences of Smoking-50 Years of Progress: A Report of the Surgeon General. 2014: Atlanta (GA).
        • Leung G.
        • et al.
        Behavioral disorders and diabetes-related outcomes among Massachusetts medicare and Medicaid beneficiaries.
        Psychiatr. Serv. 2011; 62: 659-665https://doi.org/10.1176/ps.62.6.pss6206_0659
        • Hongli Z.
        • et al.
        Joint effect of alcohol drinking and tobacco smoking on all-cause mortality and premature death in China: a cohort study.
        PLoS One. 2021; 16e0245670https://doi.org/10.1371/journal.pone.0245670
        • Wu L.T.
        • et al.
        Substance use and mental diagnoses among adults with and without type 2 diabetes: results from electronic health records data.
        Drug Alcohol Depend. 2015; 156: 162-169https://doi.org/10.1016/j.drugalcdep.2015.09.003