Systematic web monitoring of drug test subversion strategies in the United States.

As negative drug tests are frequently a condition for employment, some people who use drugs will try to subvert the testing. In this study, systematic web monitoring was used to investigate how drug test subversion is discussed online. Posts pertaining to drug test subversion were obtained from public websites and the dark web (n = 634, July-December 2021). Most information from public websites came from Twitter (65%), and 94% of dark web posts were from Reddit. The posts were manually coded to extract quantitative and qualitative information about drug test subversion tactics. Most posts discussed urine drug tests (85%), followed by hair (11%) and oral fluid (2%), and the most discussed drugs were marijuana (72%) and cocaine (7.3%). Urine drug test subversion mainly pertained to specimen substitution, with synthetic urine or urine from another person. Another strategy was to mask diluted urine by ingesting creatine. Urine adulteration was rarely discussed. Hair test subversion involved harsh treatments with products such as bleach, baking soda, and/or detergent. Hair removal was also discussed. Oral fluid test subversion focused on removing drugs from the oral cavity through vigorous brushing of teeth and tongue as well as the use of mouthwash, hydrogen peroxide, gum, and commercial detox products. This study highlights subversion strategies used by donors. Although little evidence was provided as to the effectiveness of these strategies, this information may help guide future studies and development of specimen validity testing to minimize the impact of drug test subversion attempts.

Fentanyl as a marker of illicit drug use in morphine-positive urine specimens from workplace drug testing.

Total morphine is an important urinary marker of heroin use but can also be present from prescriptions or poppy seed ingestion. In specimens with morphine concentrations consistent with poppy seed ingestion (<4,000 ng/mL), 6-acetylmorphine has served as an important marker of illicit drug use. However, as illicit fentanyl has become increasingly prevalent as a contaminant in the drug supply, fentanyl might be an alternative marker of illicit opioid use instead of or in combination with 6-acetylmorphine. The aim of this study was to quantify opiates, 6-acetylmorphine, fentanyl and fentanyl analogs in 504 morphine-positive (immunoassay 2,000 ng/mL cutoff) urine specimens from workplace drug testing. Almost half (43%) of morphine-positive specimens had morphine concentrations below 4,000 ng/mL, illustrating the need for markers to differentiate illicit drug use. In these specimens, fentanyl (22% co-positivity) was more prevalent than 6-acetylmorphine (12%). Co-positivity of 6-acetylmorphine and semi-synthetic opioids increased with morphine concentration, while fentanyl prevalence did not. In 110 fentanyl-positive specimens, the median norfentanyl concentration (1,520 ng/mL) was 9.6× higher than the median fentanyl concentration (159 ng/mL), illustrating the possibility of using norfentanyl as a urinary marker of fentanyl use. The only fentanyl analog identified was para-fluorofentanyl (n = 50), with results from most specimens consistent with para-fluorofentanyl contamination in illicit fentanyl. The results confirm the use of fentanyl by employees subject to workplace drug testing and highlight the potential of fentanyl and/or norfentanyl as important markers of illicit drug use.

Rapid Development of Specialty Population Registries and Quality Measures from Electronic Health Record Data*. An Agile Framework.

BACKGROUND: Creation of a new electronic health record (EHR)-based registry often can be a "one-off" complex endeavor: first developing new EHR data collection and clinical decision support tools, followed by developing registry-specific data extractions from the EHR for analysis. Each development phase typically has its own long development and testing time, leading to a prolonged overall cycle time for delivering one functioning registry with companion reporting into production. The next registry request then starts from scratch. Such an approach will not scale to meet the emerging demand for specialty registries to support population health and value-based care. OBJECTIVE: To determine if the creation of EHR-based specialty registries could be markedly accelerated by employing (a) a finite core set of EHR data collection principles and methods, (b) concurrent engineering of data extraction and data warehouse design using a common dimensional data model for all registries, and (c) agile development methods commonly employed in new product development. METHODS: We adopted as guiding principles to (a) capture data as a byproduct of care of the patient, (b) reinforce optimal EHR use by clinicians, (c) employ a finite but robust set of EHR data capture tool types, and (d) leverage our existing technology toolkit. Registries were defined by a shared condition (recorded on the Problem List) or a shared exposure to a procedure (recorded on the Surgical History) or to a medication (recorded on the Medication List). Any EHR fields needed - either to determine registry membership or to calculate a registry-associated clinical quality measure (CQM) - were included in the enterprise data warehouse (EDW) shared dimensional data model. Extract-transform-load (ETL) code was written to pull data at defined "grains" from the EHR into the EDW model. All calculated CQM values were stored in a single Fact table in the EDW crossing all registries. Registry-specific dashboards were created in the EHR to display both (a) real-time patient lists of registry patients and (b) EDW-generated CQM data. Agile project management methods were employed, including co-development, lightweight requirements documentation with User Stories and acceptance criteria, and time-boxed iterative development of EHR features in 2-week "sprints" for rapid-cycle feedback and refinement. RESULTS: Using this approach, in calendar year 2015 we developed a total of 43 specialty chronic disease registries, with 111 new EHR data collection and clinical decision support tools, 163 new clinical quality measures, and 30 clinic-specific dashboards reporting on both real-time patient care gaps and summarized and vetted CQM measure performance trends. CONCLUSIONS: This study suggests concurrent design of EHR data collection tools and reporting can quickly yield useful EHR structured data for chronic disease registries, and bodes well for efforts to migrate away from manual abstraction. This work also supports the view that in new EHR-based registry development, as in new product development, adopting agile principles and practices can help deliver valued, high-quality features early and often.