New York City Tax Levy.

Aims To address barriers to implementing the “Alcohol, Smoking and Substance Involvement Screening Test (ASSIST)” in medical settings, we adapted the traditional interviewer-administered (IA) ASSIST to an audio-guided computer assisted self-interview (ACASI) format. This study sought to validate the ACASI ASSIST by estimating the concordance, correlation, and agreement of scores generated using the ACASI versus the reference standard IA ASSIST. Secondary aims were to assess feasibility and compare ASSIST self-report to drug testing results. Design Participants completed the ACASI and IA ASSIST in a randomly assigned order, followed by drug testing. Setting Urban safety-net primary care clinic. Participants A total of 393 adult patients. Measurements Scores generated by the ACASI and IA ASSIST; drug testing results from saliva and hair samples. Findings Concordance between the ACASI and IA ASSIST in identifying moderate-high risk use was 92–99% for each substance class. Correlation was excellent for global scores (ICC=0.94, CI 0.92–0.95) and for substance-specific scores for tobacco (ICC=0.93, CI 0.91–0.94), alcohol (ICC=0.91, CI 0.89–0.93) and illicit drugs (ICC=0.85, CI 0.85–0.90), and good for prescription drugs (ICC=0.68, CI 0.61–0.73). Ninety-four percent of differences in global scores fell within anticipated limits of agreement. Among participants with a positive saliva test, 74% self-reported use on the ACASI ASSIST. The ACASI ASSIST required a median time of 3.7 minutes (range 0.7–15.4), and 21 (5.3%) participants requested assistance. Conclusions The computer self-administered Alcohol, Smoking and Substance Involvement Screening Test appears to be a valid alternative to the interviewer-administered approach for identifying substance use in primary care patients.

Female mortality from IMD was significantly increased compared with males, controlling for other predictors of mortality. Sex-based differences in recognition and treatment need to be evaluated in cases of meningococcal disease. Our study highlights the importance of analyzing routine surveillance data to identify and address disparities in disease incidence and outcomes.

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