Skip to main content

Advertisement

SpringerLink
Log in

Psychometric Data Linking Across HIV and Substance Use Cohorts

  • Original Paper
  • Published:
AIDS and Behavior Aims and scope Submit manuscript

Abstract

Psychometric data linking of psychological and behavioral questionnaires can facilitate the harmonization of data across HIV and substance use cohorts. Using data from the Collaborating Consortium of Cohorts Producing NIDA Opportunities (C3PNO), we demonstrate how to capitalize on previous linking work with a common linked depression metric across multiple questionnaires. Cohorts were young men who have sex with men (MSM), substance-using MSM, HIV/HCV cocaine users, and HIV-positive patients. We tested for differential item functioning (DIF) by comparing C3PNO cohort data with general population data. We also fit a mixed-effects model for depression, entering HIV-status and recent opioid/heroin use as fixed effects and cohort as a random intercept. Our results suggest a minimal level of DIF between the C3PNO cohorts and general population samples. After linking, descriptive statistics show a wide range of depression score means across cohorts. Our model confirmed an expected positive relationship between substance use and depression, though contrary to expectations, no significant association with HIV status. The study reveals the likely role of cohort differences, associated patient characteristics, study designs, and administration settings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Marshall BD, Friedman SR, Monteiro JF, Paczkowski M, Tempalski B, Pouget ER, et al. Prevention and treatment produced large decreases in HIV incidence in a model of people who inject drugs. Health Aff. (Millwood). 2014;33(3):401–9.

    Google Scholar 

  2. Crepaz N, Hess KL, Purcell DW, Hall HI. Estimating national rates of HIV infection among MSM, persons who inject drugs, and heterosexuals in the United States. AIDS. 2019;33(4):701–8.

    PubMed  Google Scholar 

  3. Fauci AS, Redfield RR, Sigounas G, Weahkee MD, Giroir BP. Ending the HIV epidemic: a plan for the United States. JAMA. 2019;321:844–5.

    PubMed  Google Scholar 

  4. Curran PJ, Hussong AM. Integrative data analysis: the simultaneous analysis of multiple data sets. Psychol Methods. 2009;14(2):81–100.

    PubMed  PubMed Central  Google Scholar 

  5. Chandler RK, Kahana SY, Fletcher B, Jones D, Finger MS, Aklin WM, et al. Data collection and harmonization in HIV research: the seek, test, treat, and retain initiative at the national institute on drug Abuse. Am J Public Health. 2015;105(12):2416–22.

    PubMed  PubMed Central  Google Scholar 

  6. Hussong AM, Curran PJ, Bauer DJ. Integrative data analysis in clinical psychology research. Annu Rev Clin Psychol. 2013;9:61–89.

    PubMed  PubMed Central  Google Scholar 

  7. McHugh RK, Rasmussen JL, Otto MW. Comprehension of self-report evidence-based measures of anxiety. Depress Anxiety. 2011;28(7):607–14.

    PubMed  Google Scholar 

  8. Dorans NJ. Linking scores from multiple health outcome instruments. Qual Life Res. 2007;16(1):85–94.

    PubMed  Google Scholar 

  9. Kolen MJ, Brennan RL. Test equating, scaling, and linking. New York: Springer; 2004.

    Google Scholar 

  10. Kolen MJ, Brennan RL. Observed score equating using the random groups design. Test equating, scaling, and linking. New York: Springer; 2014. p. 29–63.

    Google Scholar 

  11. De Ayala RJ. The theory and practice of item response theory. New York: Guilford Publications; 2013.

    Google Scholar 

  12. Embretson SE, Reise SP. Item response theory. London: Psychology Press; 2013.

    Google Scholar 

  13. Choi SW, Schalet B, Cook KF, Cella D. Establishing a common metric for depressive symptoms: linking the BDI-II, CES-D, and PHQ-9 to PROMIS depression. Psychol Assess. 2014;26(2):513.

    PubMed  PubMed Central  Google Scholar 

  14. Schalet BD, Cook KF, Choi SW, Cella D. Establishing a common metric for self-reported anxiety: linking the MASQ, PANAS, and GAD-7 to PROMIS anxiety. J Anxiety Disord. 2014;28(1):88–96.

    PubMed  Google Scholar 

  15. Cook KF, Schalet BD, Kallen MA, Rutsohn JP, Cella D. Establishing a common metric for self-reported pain: linking BPI pain interference and SF-36 bodily pain subscale scores to the PROMIS pain interference metric. Qual Life Res. 2015;24(10):2305–18.

    PubMed  PubMed Central  Google Scholar 

  16. Rose JS, Dierker LC, Hedeker D, Mermelstein R. An integrated data analysis approach to investigating measurement equivalence of DSM nicotine dependence symptoms. Drug Alcohol Depend. 2013;129(1–2):25–322.

    CAS  PubMed  Google Scholar 

  17. Greenbaum PE, Wang W, Henderson CE, Kan L, Hall K, Dakof GA, et al. Gender and ethnicity as moderators: integrative data analysis of multidimensional family therapy randomized clinical trials. J Fam Psychol. 2015;29(6):919–30.

    PubMed  PubMed Central  Google Scholar 

  18. Hussong AM, Gottfredson NC, Bauer DJ, Curran PJ, Haroon M, Chandler R, et al. Approaches for creating comparable measures of alcohol use symptoms: harmonization with eight studies of criminal justice populations. Drug Alcohol Depend. 2019;194:59–68.

    PubMed  Google Scholar 

  19. Janulis P, Newcomb ME, Sullivan P, Mustanski B. Evaluating HIV knowledge questionnaires among men who have sex with men: a Multi-Study item response theory analysis. Arch Sex Behav. 2018;47(1):107–19.

    PubMed  Google Scholar 

  20. Kaat AJ, Newcomb ME, Ryan DT, Mustanski B. Expanding a common metric for depression reporting: linking two scales to PROMIS® depression. Qual Life Res. 2016;26:1119–28.

    PubMed  PubMed Central  Google Scholar 

  21. Scherrer JF, Svrakic DM, Freedland KE, Chrusciel T, Balasubramanian S, Bucholz KK, et al. Prescription opioid analgesics increase the risk of depression. J Gen Intern Med. 2014;29(3):491–9.

    PubMed  Google Scholar 

  22. Gonzalez JS, Batchelder AW, Psaros C, Safren SA. Depression and HIV/AIDS treatment nonadherence: a review and meta-analysis. J Acquir Immune Defic Syndr. 2011. https://doi.org/10.1097/QAI.0b013e31822d490a.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Gorbach PM, Siminski S, Ragsdale A, the C3PNO Investigators. Cohort profile: the collaborating consortium of cohorts producing NIDA opportunities (C3PNO). Int J Epidemiol. In press.

  24. Pilkonis PA, Choi SW, Reise SP, Stover AM, Riley WT, Cella D. Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger. Assessment. 2011;18(3):263–83.

    PubMed  PubMed Central  Google Scholar 

  25. Pilkonis PA, Choi SW, Salsman JM, Butt Z, Moore TL, Lawrence SM, et al. Assessment of self-reported negative affect in the NIH Toolbox. Psychiatry Res. 2013;206(1):88–97.

    PubMed  Google Scholar 

  26. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol. 2010;63(11):1179–94.

    PubMed  PubMed Central  Google Scholar 

  27. Cella D, Choi SW, Condon DM, Schalet B, Hays RD, Rothrock NE, et al. PROMIS® adult health profiles: efficient short-form measures of seven health domains. Value Health. 2019;22(5):537–44.

    PubMed  PubMed Central  Google Scholar 

  28. Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH. The PHQ-8 as a measure of current depression in the general population. J Affect Disord. 2009;114(1–3):163–73.

    PubMed  Google Scholar 

  29. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.

    Google Scholar 

  31. Myers JK, Weissman MM. Use of a self-report symptom scale to detect depression in a community sample. Am J Psychiatry. 1980;137:1081–4.

    CAS  PubMed  Google Scholar 

  32. Naughton MJ, Wiklund I. A critical review of dimension-specific measures of health-related quality of life in cross-cultural research. Qual Life Res. 1993;2(6):397–43232.

    CAS  PubMed  Google Scholar 

  33. Zimmerman M, Coryell W. Screening for major depressive disorder in the community: a comparison of measures. Psychol Assess. 1994;6(1):71.

    Google Scholar 

  34. Carleton RN, Thibodeau MA, Teale MJ, Welch PG, Abrams MP, Robinson T, et al. The center for epidemiologic studies depression scale: a review with a theoretical and empirical examination of item content and factor structure. PLoS ONE. 2013;8(3):e58067.

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Revelle W. Psych: procedures for psychological, psychometric, and personality research. 1.3.2 ed. Evanston, IL: The comprehensive R archive network; 2013.

  36. Schmid J, Leiman JM. The development of hierarchical factor solutions. Psychometrika. 1957;22(1):53–61.

    Google Scholar 

  37. McDonald RP. TEst theory: a unified treatment. Mahwah: L. Erlbaum Associates; 1999.

    Google Scholar 

  38. Zinbarg RE, Revelle W, Yovel I, Li W. Cronbach’s α, Revelle’s β, and McDonald’s ω H: their relations with each other and two alternative conceptualizations of reliability. Psychometrika. 2005;70(1):123–33.

    Google Scholar 

  39. Reise SP, Scheines R, Widaman KF, Haviland MG. Multidimensionality and structural coefficient bias in structural equation modeling: a bifactor perspective. Educ Psychol Meas. 2013;73(1):5–26.

    Google Scholar 

  40. Choi SW, Gibbons LE, Crane PK. Lordif: an R package for detecting differential item functioning using iterative hybrid ordinal logistic regression/item response theory and Monte Carlo simulations. J Stat Softw. 2011;39(8):1.

    PubMed  PubMed Central  Google Scholar 

  41. Teresi JA. Different approaches to differential item functioning in health applications: advantages, disadvantages and some neglected topics. Med Care. 2006;44:S152–S170170.

    PubMed  Google Scholar 

  42. Samejima F. Estimation of latent ability using a response pattern of graded scores. Psychometrika Monogr Suppl. 1969. https://doi.org/10.1002/j.2333-8504.1968.tb00153.x.

    Article  Google Scholar 

  43. Kleinman M, Teresi JA. Differential item functioning magnitude and impact measures from item response theory models. Psychol Test Assess Model. 2016;58(1):79.

    PubMed  PubMed Central  Google Scholar 

  44. Hays RD, Calderón JL, Spritzer KL, Reise SP, Paz SH. Differential item functioning by language on the PROMIS® physical functioning items for children and adolescents. Qual Life Res. 2018;27(1):235–47.

    PubMed  Google Scholar 

  45. Kroenke K, Yu Z, Wu J, Kean J, Monahan PO. Operating characteristics of PROMIS four-item depression and anxiety scales in primary care patients with chronic pain. Pain Med. 2014;15(11):1892–901.

    PubMed  PubMed Central  Google Scholar 

  46. Pilkonis PA, Yu L, Dodds NE, Johnston KL, Maihoefer CC, Lawrence SM. Validation of the depression item bank from the Patient-Reported Outcomes Measurement Information System (PROMIS®) in a three-month observational study. J Psychiatr Res. 2014;56:112–9.

    PubMed  PubMed Central  Google Scholar 

  47. Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1).

  48. Bock RD, Mislevy RJ. Adaptive EAP estimation of ability in a microcomputer environment. Appl Psychol Meas. 1982;6(4):431–44.

    Google Scholar 

  49. Manea L, Gilbody S, McMillan D. Optimal cut-off score for diagnosing depression with the Patient Health Questionnaire (PHQ-9): a meta-analysis. Can Med Assoc J. 2012;184(3):E191–E196196.

    Google Scholar 

  50. Kroenke K, Spitzer RL, Williams JB, Löwe B. The patient health questionnaire somatic, anxiety, and depressive symptom scales: a systematic review. Gen Hosp Psychiatry. 2010;32(4):345–59.

    PubMed  Google Scholar 

  51. Cook KF, Jensen SE, Schalet BD, Beaumont JL, Amtmann D, Czajkowski S, et al. PROMIS measures of pain, fatigue, negative affect, physical function, and social function demonstrated clinical validity across a range of chronic conditions. J Clin Epidemiol. 2016;73:89–102.

    PubMed  PubMed Central  Google Scholar 

  52. Liegl G, Wahl I, Berghöfer A, Nolte S, Pieh C, Rose M, et al. Using Patient Health Questionnaire-9 item parameters of a common metric resulted in similar depression scores compared to independent item response theory model reestimation. J Clin Epidemiol. 2016;71:25–34.

    PubMed  Google Scholar 

  53. Cook KF, Kallen MA, Bombardier C, Bamer AM, Choi SW, Kim J, et al. Do measures of depressive symptoms function differently in people with spinal cord injury versus primary care patients: the CES-D, PHQ-9, and PROMIS®-D. Qual Life Res. 2017;26(1):139–48.

    PubMed  Google Scholar 

  54. Askew RL, Kim J, Chung H, Cook KF, Johnson KL, Amtmann D. Development of a crosswalk for pain interference measured by the BPI and PROMIS pain interference short form. Qual Life Res. 2013;22(10):2769–76.

    PubMed  Google Scholar 

  55. Revicki D, Hays RD, Cella D, Sloan J. Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. J Clin Epidemiol. 2008;61(2):102–9.

    PubMed  Google Scholar 

  56. Purvis TE, Neuman BJ, Riley LH III, Skolasky RL. Discriminant ability, concurrent validity, and responsiveness of PROMIS health domains among patients with lumbar degenerative disease undergoing decompression with or without arthrodesis. Spine. 2018;43(21):1512–20.

    PubMed  Google Scholar 

  57. Jensen RE, Moinpour CM, Potosky AL, Lobo T, Hahn EA, Hays RD, et al. Responsiveness of 8 Patient-Reported Outcomes Measurement Information System (PROMIS) measures in a large, community-based cancer study cohort. Cancer. 2016;123:327–35.

    PubMed  PubMed Central  Google Scholar 

  58. Remien RH, Stirratt MJ, Nguyen N, Robbins RN, Pala AN, Mellins CA. Mental health and HIV/AIDS: the need for an integrated response. AIDS (London, England). 2019;33(9):1411.

    Google Scholar 

Download references

Acknowledgements

We thank all C3PNO Cohort Principal Investigators for participation in the consortium and making this study possible: Kora DeBeck, Kanna Hayashi, Thomas Kerr, Gregory Kirk, Shenghan Lai, Shruti Mehta, M-J Milloy, and Jeanne Keruly. This project is supported by the National Institute of Drug Abuse (NIDA) of the National Institutes of Health under Award Numbers U24DA044554, U01DA036935, U01DA040381 U01DA036267, U01DA036939, U01DA036926, and P30 MH058107.

Author information

Authors and Affiliations

  1. Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, 633 N. St. Clair, 19th Floor, Chicago, IL, 60611, USA

    Benjamin D. Schalet, Patrick Janulis & Brian Mustanski

  2. Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, USA

    Michele D. Kipke

  3. Institute for Sexual and Gender Minority Health and Wellbeing, Northwestern University, Chicago, USA

    Patrick Janulis & Brian Mustanski

  4. Department of Family Medicine, David Geffen School of Medicine, University of California, Los Angeles, USA

    Steven Shoptaw

  5. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, USA

    Richard Moore

  6. Department of Dietetics and Nutrition, Robert Stempel College of Public Health, Florida International University, Miami, USA

    Marianna Baum

  7. Frontier Science Foundation, Brookline, MA, USA

    Soyeon Kim & Suzanne Siminski

  8. Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, USA

    Amy Ragsdale & Pamina M. Gorbach

  9. Division of Infectious Diseases, David Geffen School of Medicine, University of California, Los Angeles, USA

    Pamina M. Gorbach

Authors
  1. Benjamin D. Schalet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Janulis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Michele D. Kipke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brian Mustanski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Steven Shoptaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Richard Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marianna Baum
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Soyeon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Suzanne Siminski
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Amy Ragsdale
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Pamina M. Gorbach
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Benjamin D. Schalet.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schalet, B.D., Janulis, P., Kipke, M.D. et al. Psychometric Data Linking Across HIV and Substance Use Cohorts. AIDS Behav 24, 3215–3224 (2020). https://doi.org/10.1007/s10461-020-02883-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10461-020-02883-5

Keywords

Search

Navigation