Assessment of Injection Drug Use Based on Diagnostic Codes in Administrative Datasets M Kuo 1, NZ Janjua 1,2, AYW Yu 1, N Islam 1,2, H Samji 1, JA Buxton 1,2 , Z Butt 1,2, M Tyndall 1,2, J Wong 1,2 , M Krajden 1,2,3, & The BC-HTC Team 1 1 BC Centre for Disease Control, 2 University of British Columbia, 3 BCCDC Public Health Laboratory Background Results Results (cont’d)   Table 1. Sensitivity and Specificity of Multiple Algorithms to Identify PWID Description SENS SPEC PPV NPV Illicit Drug Use: Injectables and Non-Injectables A. 1 MSP OR 1 DAD code 88.3 63.4 51.4 92.5 B. 2 MSP OR 1 DAD code 85.4 67.9 53.8 91.4 C. 3 MSP OR 1 DAD code 83.9 69.6 54.7 90.8 Illicit Drug Use: Injectables Only D. 1 MSP OR 1 DAD code for injectables 78.2 72.3 55.3 E. 1 MSP OR 1 DAD code OR OST code (MSP/PharmaNet) 82.8 70.1 54.8 90.3 F. 1 MSP OR 1 DAD code for injectables AND injection-related infection within 2yrs of drug use 59.7 81.8 59 82.3 G. 1 MSP OR 1 DAD code for injectables OR OST code AND injection-related infection within 2yrs of drug use 63.8 80.6 83.6 H. 1 MSP OR 1 DAD code for injectables AND injection-related infection within 1yr of drug use 55.8 84.1 60.6 81.3 I. 1 MSP OR 1 DAD code for injectables OR OST code AND injection-related infection within 1yrs of drug use 60.8 82.5 60.3 The British Columbia Hepatitis Testers Cohort (BC-HTC) is a large data linkage designed to assess and monitor HCV in BC. It includes 1.4 million persons tested for HCV or HIV and their data on medical visits, hospitalizations and drug prescriptions since the early 1990’s to 2012.1 People who inject drugs (PWID) are at high risk for HIV, HBV, HCV, and drug overdose, endocarditis, cellulitis and abscesses. Further, PWID experience poor health outcomes due to delayed access to effective treatment, continuation of drug use, higher prevalence of mental illness, and negative life events such as violence, poverty, and homelessness.2 Thus, PWID are a priority population for monitoring across the continuum of HCV testing, diagnosis, care, and treatment. However, diagnostic codes in administrative datasets do not differentiate injection drug use from non-injection drug use. Figure 1. Percent HCV Positivity Among PWID in a Given Year We used four algorithms to identify annual numbers of PWID and, among each year group, identified new and prior positive HCV cases. (Figure 1) Percent HCV positivity was highest in those identified using IDU codes combined with OST and IRI within 1 year (76.7% in 2012). (Figure 1) Lowest HCV positivity was in those identified by drug use codes only (56.9% in 2012). (Figure 1) Algorithms including non-injectables were the most sensitive, correctly identifying a high proportion of PWID but, due to lower specificity, also have more false positives (i.e. did not correctly identify a proportion of those who did not inject). (Table 1) Algorithms focussing on injectables only had higher specificity which improved with the addition of IRI within a timeframe. (Table 1) The addition of OST increased sensitivity, identifying more of the PWID, but slightly increased false positives. (Table 1) Purpose Conclusions Our aim was to develop optimal algorithms to identify PWID in the BC-HTC. The inclusion of IRI ≤1yr reduces false positives and provides timing of drug use; whereas OST may capture more previous vs. current drug use The optimum algorithm depends on what is being assessed - how important it is to have only PWID in the sample or to know if drug use if current vs. remote Identification of PWID will inform: Difficult-to-estimate geo-specific prevalences of PWIDs in order to allocate resources and establish policies to reduce harm The HCV and HIV cascade of care among PWID Prevalence in the PWID population of other important comorbidities such as severe and persistent mental illness Methods   Table 2. Comparison of Four Algorithms Estimating the Prevalence of PWID in the BC-HTC, by Year-Periods of IDU, 1990-2012 Year Period of IDU E: IDU* + OST D: IDU* Only I: IRI ≤ 1 year of IDU* OR OST H: IRI ≤ 1 year of IDU* IDU 1990 - 1994 10,439 9,679 2,722 2,426 IDU 1995-1999 21,887 18,910 6,892 5,970 IDU 2000-2004 31,775 25,215 10,041 7,603 IDU 2005-2009 42,910 35,049 15,767 12,397 IDU 2010-2012 35,872 25,054 10,038 6,928 IDU at any time 1990-2012 87,024 79,139 30,931 26,820 * Injectables only In a subset of the BC-HTC, self-reported injection drug use (no/yes and timeframe) was collected during public health follow-up of a new diagnosis of HCV or HIV. This data was used as the gold standard against which 9 algorithms were tested for their ability to correctly identify PWID and differentiate those who did not inject drugs. Algorithm components included: ICD9/10 codes in physician billing (MSP) and hospital data (DAD) for illicit drug use (injectables and non-injectables) MSP fee item and PharmaNet drug codes for Opioid Substitution Treatment (OST) ICD9/10 codes for injection-related infections (IRI) within 1 or 2 years of eligible drug codes Primary validation outcomes were sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Secondary outcomes included comparisons of percent HCV positivity in those identified by different algorithms. Increasing Sensitivity Increasing Specificity Applying four of the algorithms to the BC-HTC produced varying PWID populations based on the sensitivity and specificity. (Table 2) Considering the most specific algorithm (least false positives), the PWID subgroup that can be used for future analyses includes 26,820 persons. Considering the most sensitive algorithm, there were 87,024 PWIDs. (Table 2) References Janjua NZ, Kuo M, Chong M, Yu A, Alvarez M, et al. (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(3): e0150176. doi: 10.1371/journal.pone.0150176 2. Tempalski B, Pouget ER, Cleland CM, Brady JE, Cooper HLF, Hall HI, et al. (2013) Trends in the Population Prevalence of People Who Inject Drugs in the US Metropolitan Areas 1992-2007. PLoS ONE 8(6): e64789. doi:10.1371/journal.pone.0064789 Contact Information: Dr. Naveed Janjua, Senior Scientist, Clinical Prevention Services, BCCDC naveed.janjua@bccdc.ca Conflict of Interest Disclosure: Authors have no conflicts of interest CAHR 2016: 1482-7300