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Time series-based bibliometric analysis of a systematic review of multidisciplinary care for opioid dose reduction: exploring the origins of the North American opioid crisis

Author

Listed:
  • Abhimanyu Sud

    (University of Toronto
    Lunenfeld-Tanenbaum Research Institute, Sinai Health
    Institute of Health Policy, Management and Evaluation, University of Toronto)

  • Darren K. Cheng

    (Lunenfeld-Tanenbaum Research Institute, Sinai Health)

  • Rahim Moineddin

    (University of Toronto)

  • Erin Zlahtic

    (Western University)

  • Ross Upshur

    (University of Toronto
    Lunenfeld-Tanenbaum Research Institute, Sinai Health
    Institute of Health Policy, Management and Evaluation, University of Toronto
    University of Toronto)

Abstract

Bibliometric analyses of systematic reviews offer unique opportunities to explore the character of specific scientific fields. In this time series-based analysis, dynamics of multidisciplinary care for chronic pain and opioid prescribing are analyzed over a forty-four year time span. Three distinct periods are identified, each defined by distinct research areas, as well as priorities regarding the use of opioids and the appropriate management of chronic pain. These scientometrically defined periods align with timelines identified previously by narrative historical accounts. Through cross-correlation with a mortality time series, a significant two-year lag between opioid overdose mortality and citation dynamics is identified between 2004 and 2019. This analysis demonstrates a bidirectional relationship between the scientific literature and the North American opioid overdose crisis, suggesting that the scientific literature is both reflective and generative of an important health and social phenomenon. A scientometric phenomenon of memory lapse, namely an overt and prolonged failure to cite older relevant literature, is identified using a metric of mean time to citation. It is proposed that this metric can be used to analyze changes in emerging literature and thus predict the nature of clinical and policy responses to the opioid crisis, and thus potentially to other health and social phenomena.

Suggested Citation

  • Abhimanyu Sud & Darren K. Cheng & Rahim Moineddin & Erin Zlahtic & Ross Upshur, 2021. "Time series-based bibliometric analysis of a systematic review of multidisciplinary care for opioid dose reduction: exploring the origins of the North American opioid crisis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 8935-8955, November.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04154-z
    DOI: 10.1007/s11192-021-04154-z
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    References listed on IDEAS

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    1. Sonia E. Monroy & Hernando Diaz, 2018. "Time series-based bibliometric analysis of the dynamics of scientific production," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(3), pages 1139-1159, June.
    2. Paul T E Cusack, 2020. "On Pain," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 31(3), pages 24253-24254, October.
    3. Mike Thelwall, 2019. "Are classic references cited first? An analysis of citation order within article sections," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 723-731, August.
    4. Van Zee, A., 2009. "The promotion and marketing of oxycontin: Commercial triumph, public health tragedy," American Journal of Public Health, American Public Health Association, vol. 99(2), pages 221-227.
    5. Webster, Fiona & Rice, Kathleen & Sud, Abhimanyu, 2020. "A critical content analysis of media reporting on opioids: The social construction of an epidemic," Social Science & Medicine, Elsevier, vol. 244(C).
    6. Geels, Frank W. & Schot, Johan, 2007. "Typology of sociotechnical transition pathways," Research Policy, Elsevier, vol. 36(3), pages 399-417, April.
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    Cited by:

    1. Prem Kumar Singh, 2022. "t-index: entropy based random document and citation analysis using average h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 637-660, January.

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