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Identifying injection drug use and estimating population size of people who inject drugs using healthcare administrative datasets

Janjua, Naveed Zafar, Islam, Nazrul, Kuo, Margot, Yu, Amanda, Wong, Stanley, Butt, Zahid A., Gilbert, Mark, Buxton, Jane, Chapinal, Nuria, Samji, Hasina, Chong, Mei, Alvarez, Maria, Wong, Jason, Tyndall, Mark W., Krajden, Mel·2018·International Journal of Drug Policy
DOI 10.1016/j.drugpo.2018.02.001
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Abstract

Background Large linked healthcare administrative datasets could be used to monitor programs providing prevention and treatment services to people who inject drugs (PWID). However, diagnostic codes in administrative datasets do not differentiate non-injection from injection drug use (IDU). We validated algorithms based on diagnostic codes and prescription records representing IDU in administrative datasets against interview-based IDU data. Methods The British Columbia Hepatitis Testers Cohort (BC-HTC) includes ∼1.7 million individuals tested for HCV/HIV or reported HBV/HCV/HIV/tuberculosis cases in BC from 1990 to 2015, linked to administrative datasets including physician visit, hospitalization and prescription drug records. IDU, assessed through interviews as part of enhanced surveillance at the time of HIV or HCV/HBV diagnosis from a subset of cases included in the BC-HTC (n = 6559), was used as the gold standard. ICD-9/ICD-10 codes for IDU and injecting-related infections (IRI) were grouped with records of opioid substitution therapy (OST) into multiple IDU algorithms in administrative datasets. We assessed the performance of IDU algorithms through calculation of sensitivity, specificity, positive predictive, and negative predictive values. Results Sensitivity was highest (90-94%), and specificity was lowest (42-73%) for algorithms based either on IDU or IRI and drug misuse codes. Algorithms requiring both drug misuse and IRI had lower sensitivity (57-60%) and higher spec…

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Research works printed but not linked: 36

  1. Bagheri Amiri, 2016, HIV, HBV and HCV coinfection prevalence in Iran – A systematic review and meta-analysis, PloS One, 11, e0151946, 10.1371/journal.pone.0151946 · DOI
  2. Bansal, 2016, Big data for infectious disease surveillance and modeling, The Journal of Infectious Diseases, 214, S375, 10.1093/infdis/jiw400 · DOI
  3. Butt, 2017, A syndemic approach to assess the effect of substance use and social disparities on the evolution of HIV/HCV infections in British Columbia, PLoS One, 12, e0183609, 10.1371/journal.pone.0183609 · DOI
  4. Cooper, 2007, Nationwide increase in the number of hospitalizations for illicit injection drug use-related infective endocarditis, Clinical Infectious Diseases, 45, 1200, 10.1086/522176 · DOI
  5. Degenhardt, 2016, Estimating the burden of disease attributable to injecting drug use as a risk factor for HIV, hepatitis C, and hepatitis B: Findings from the Global Burden of Disease Study 2013, Lancet Infectious Diseases, 16, 1385, 10.1016/S1473-3099(16)30325-5 · DOI
  6. Degenhardt, 2017, Global prevalence of injecting drug use and sociodemographic characteristics and prevalence of HIV, HBV, and HCV in people who inject drugs: A multistage systematic review, Lancet Glob Health, 5, e1192, 10.1016/S2214-109X(17)30375-3 · DOI
  7. Gavrielov-Yusim, 2014, Use of administrative medical databases in population-based research, Journal of Epidemiology and Community Health, 68, 283, 10.1136/jech-2013-202744 · DOI
  8. Heinzerling, 2006, Hospital utilization for injection drug use-related soft tissue infections in urban versus rural counties in California, Journal of Urban Health, 83, 176, 10.1007/s11524-005-9021-6 · DOI
  9. Hope, 2013, Prevalence of, and risk factors for, HIV, hepatitis B and C infections among men who inject image and performance enhancing drugs: A cross-sectional study, BMJ Open, 3
  10. Hudson, 2016, Comparative effectiveness research with administrative health data in rheumatoid arthritis, Nature Reviews Rheumatology, 12, 358, 10.1038/nrrheum.2016.34 · DOI
  11. Islam, 2017, Incidence, risk factors, and prevention of hepatitis C reinfection: A population-based cohort study, The Lancet Gastroenterology & Hepatology, 2, 200, 10.1016/S2468-1253(16)30182-0 · DOI
  12. Iversen, 2015, HIV: HCV and health-related harms among women who inject drugs: Implications for prevention and treatment, Journal of Acquired Immune Deficiency Syndromes (1999), 69, S176, 10.1097/QAI.0000000000000659 · DOI
  13. Janjua, 2017, Long-term effect of sustained virological response on hepatocellular carcinoma in patients with hepatitis C in Canada, Journal of Hepatology, 66, 504, 10.1016/j.jhep.2016.10.028 · DOI
  14. Janjua, 2017, Shift in disparities in hepatitis C treatment from interferon to DAA era: A population-based cohort study, Journal of Viral Hepatitis, 24, 624, 10.1111/jvh.12684 · DOI
  15. Janjua, 2016, Assessing hepatitis C burden and treatment effectiveness through the British Columbia hepatitis testers cohort (BC-HTC): Design and characteristics of linked and unlinked participants, PLoS One, 11, e0150176, 10.1371/journal.pone.0150176 · DOI
  16. Janjua, 2016, The population level cascade of care for hepatitis C in British Columbia, Canada: The BC hepatitis testers cohort (BC-HTC), EBioMedicine, 12, 189, 10.1016/j.ebiom.2016.08.035 · DOI
  17. Janjua, 2016, Twin epidemics of new and prevalent hepatitis C infections in Canada: BC hepatitis testers cohort, BMC Infectious Diseases, 16, 334, 10.1186/s12879-016-1683-z · DOI
  18. Larney, 2013, The contributions of viral hepatitis and alcohol to liver-related deaths in opioid-dependent people, Drug and Alcohol Dependence, 131, 252, 10.1016/j.drugalcdep.2012.11.012 · DOI
  19. Larney, 2015, Defining populations and injecting parameters among people who inject drugs: Implications for the assessment of hepatitis C treatment programs, International Journal of Drug Policy, 26, 950, 10.1016/j.drugpo.2015.07.010 · DOI
  20. Larney, 2015, Defining populations and injecting parameters among people who inject drugs: Implications for the assessment of hepatitis C treatment programs, International Journal of Drug Policy, 26, 950, 10.1016/j.drugpo.2015.07.010 · DOI
  21. Larney, 2017, A systematic review of injecting-related injury and disease among people who inject drugs, Drug and Alcohol Dependence, 171, 39, 10.1016/j.drugalcdep.2016.11.029 · DOI · Paper page
  22. Leclerc, 2014, Estimating the size of the population of persons who inject drugs in the island of Montreal, Canada: Using a six-source capture-recapture model, Drug and Alcohol Dependence, 142, 174, 10.1016/j.drugalcdep.2014.06.022 · DOI
  23. Mazzali, 2015, Use of administrative data in healthcare research, Internal and Emergency Medicine, 10, 517, 10.1007/s11739-015-1213-9 · DOI
  24. McInnes, 2009, HIV/AIDS in Vancouver, British Columbia: A growing epidemic, Harm Reduction Journal, 6, 5, 10.1186/1477-7517-6-5 · DOI
  25. Midgard, 2016, HCV epidemiology in high-risk groups and the risk of reinfection, Journal of Hepatology, 65, S33, 10.1016/j.jhep.2016.07.012 · DOI
  26. Miller, 2011, The Cedar Project: Risk factors for transition to injection drug use among young urban Aboriginal people, CMAJ, 183, 1147, 10.1503/cmaj.101257 · DOI
  27. Nelson, 2011, Global epidemiology of hepatitis B and hepatitis C in people who inject drugs: Results of systematic reviews, Lancet, 378, 571, 10.1016/S0140-6736(11)61097-0 · DOI
  28. Nishijima, 2013, Illicit drug use is a significant risk factor for loss to follow up in patients with HIV-1 infection at a large urban HIV clinic in Tokyo, PloS One, 8, e72310, 10.1371/journal.pone.0072310 · DOI
  29. Pampalon, 2012, An area-based material and social deprivation index for public health in Quebec and Canada, Canadian Journal of Public Health. Revue Canadienne de Santé Publique, 103, S17, 10.1007/BF03403824 · DOI
  30. Platt, 2016, Prevalence and burden of HCV co-infection in people living with HIV: A global systematic review and meta-analysis, Lancet Infectious Diseases, 16, 797, 10.1016/S1473-3099(15)00485-5 · DOI
  31. Prussing, 2015, HIV and viral hepatitis co-infection in New York City: 2000–2010: Prevalence and case characteristics, Epidemiology and Infection, 143, 1408, 10.1017/S0950268814002209 · DOI
  32. Ray Saraswati, 2015, HIV, Hepatitis B and C among people who inject drugs: High prevalence of HIV and Hepatitis C RNA positive infections observed in Delhi, India, BMC Public Health, 15, 726, 10.1186/s12889-015-2003-z · DOI
  33. Ruiz, 2016, Using capture-recapture methods to estimate the population of people who inject drugs in Washington, DC, AIDS and Behavior, 20, 363, 10.1007/s10461-015-1085-z · DOI
  34. Takahashi, 2010, US hospitalizations and costs for illicit drug users with soft tissue infections, Journal of Behavioral Health Services and Research, 37, 508, 10.1007/s11414-009-9177-z · DOI
  35. Tookes, 2015, A cost analysis of hospitalizations for infections related to injection drug use at a county safety-net hospital in Miami, Florida, PloS One, 10, e0129360, 10.1371/journal.pone.0129360 · DOI · Paper page
  36. Xu, 2014, Estimating the number of injection drug users in greater Victoria, Canada using capture-recapture methods, Harm Reduction Journal, 11, 9, 10.1186/1477-7517-11-9 · DOI

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Metadata

Authors
Janjua, N. Z., Islam, N., Kuo, M., Yu, A., Wong, S., Butt, Z. A., Gilbert, M., Buxton, J., Chapinal, N., Samji, H., Chong, M., Alvarez, M., Wong, J., Tyndall, M. W., & Krajden, M.
Venue
International Journal of Drug Policy
Publisher
Elsevier BV
Published
2018-05
Volume
55
Pages
31-39
ISSN
0955-3959
DOI
10.1016/j.drugpo.2018.02.001
Cited by
102
References
2
Language
en
Licence
Funders
  • Canadian Institutes of Health Research (NHC-142832, PHE-141773)
  • Public Health Agency of Canada
  • Michael Smith Foundation for Health Research

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CoChat. (2026). Citation check: Identifying injection drug use and estimating population size of people who inject drugs using healthcare administrative datasets. https://app.cochat.ai/papers/10.1016/j.drugpo.2018.02.001
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Janjua, N. Z., Islam, N., Kuo, M., Yu, A., Wong, S., Butt, Z. A., Gilbert, M., Buxton, J., Chapinal, N., Samji, H., Chong, M., Alvarez, M., Wong, J., Tyndall, M. W., & Krajden, M. (2018). Identifying injection drug use and estimating population size of people who inject drugs using healthcare administrative datasets. International Journal of Drug Policy. https://doi.org/10.1016/j.drugpo.2018.02.001