Mission and The DCLA Model

Drug Checking Los Angeles is a community-based public health program and research study. The project aims to reduce the harms associated with drug use by providing people who use drugs with accurate information about the contents of the illicit supply. By operating at the intersection of harm reduction and analytical chemistry, DCLA both informs public health monitoring, and provides individuals with information they can use to stay safer when using substances. For more information , please visit the DCLA homepage.

Sample Collection

Data are derived from community-based drug checking services operating across Los Angeles County. Participants provide small quantities of residue, or used paraphernalia (such as baggies or cookers) for analysis. Depending on resource availability and what participants believe they are testing, a combination of immunoassay-based testing strips and Fourier Transform Infrared (FTIR) Spectroscopy may be offerred as point-of-care services, as well as send-out qualitative (DART-MS) and quantitative (LC-MS) testing performed at the National Insitute for Standards and Technology (NIST).

Data Used in the Viz

Data used in the viz tool represent gold standard technolgies, rather than point-of-care tests that are less precise. These include LC-MS and DART-MS

Samples are processed using rigorous extraction and dilution protocols at NIST and analyzed against certified reference materials. This allows for the quantification of mass-percentage concentration (purity) for primary analytes such as fentanyl and methamphetamine or emerging additives like BTMPS or xylazine.

Data Categorization

Constituents are categorized into functional drug classes to assist in interpretations. The 'expected drug' metric is a critical variable, representing the participant’s self-reported intent at the time of sample submission. By cross-referencing this expectation with DART-MS confirmation, we calculate mismatch rates.

Statistical Analysis of Potency and Prevalence

The visualizations employ several statistical conventions to note. Concentration Histograms utilize binning strategies to visualize the range of purity values. Time Trends utilize quarterly aggregation to smooth short-term noise while preserving seasonal or supply-chain-driven shifts. Data points representing low-frequency analytes may be suppressed to protect participant anonymity and ensure statistical validity.

Dynamic Nature of the Live Dataset

Important Notice: The data visualized in this dashboard are updated on a regular cycle as new results are returned from the laboratory. Because this is a relatively 'live' surveillance tool, the values, means, and prevalence rates shown here may not align perfectly with previously published findings in the academic papers listed below. Discrepancies typically arise from the continuous addition of new samples, but in some cases could also reflect retrospective re-analysis of older samples as new reference standards become available, or refinements in drug class categorization.