ABSTRACT
BACKGROUND: Methadone reduces opioid-related harms and overdose deaths in those with opioid use disorder (OUD) yet in the US is restricted to federal and state-regulated opioid treatment programs (OTPs). Methadone access remains limited, particularly in rural settings. We sought to determine prescriber and practice characteristics associated with support for provision of methadone through office-based settings. METHODS: We performed a secondary analysis of the Opioid Use Disorder Provider COVID-19 Survey, a survey collaboratively developed with multistakeholder input to explore the impact of COVID-19-related practice changes among X-waivered buprenorphine prescribers. Data were collected from July to August 2020 electronically and analyses herein were restricted to prescribers who provided outpatient, longitudinal care for adults with OUD. The outcome variable was selecting “The opportunity for patients to receive office-based methadone” when asked “Which of the pandemic-related federal policy changes or new policy changes would you like to be continued after the pandemic?” Sequential multivariable logistic regression analyses were performed to determine prescriber and practice characteristics associated with support for the opportunity for patients to receive office-based methadone. RESULTS: Among invited participants, 1,900 initiated and completed the survey. Among the 739 respondents included in the analysis, 52% were men, 60% were ≥50 years old, 81% were White, 39% were board certified in Addiction Medicine/Psychiatry, 44% were practicing in family medicine or internal medicine, and 20% in psychiatry. Nineteen percent had prescribed medications to treat OUD (MOUD) for ≥15 years, 20% had ordered methadone previously, and 21% worked in OTPs. Twenty-nine percent indicated support for office-based methadone. In sequential multivariable logistic regression models, factors associated with support for office-based methadone, compared to being White, were being Asian (AOR=2.23;95% [CI] = 1.01, 5.04), Black/African-American (AOR=3.36;95% [CI] = 1.30, 8.71);having prescribed MOUD for ≥15 years (OR=2.06;95% [CI] = 1.15, 3.66) compared to 0-5 years;having ordered methadone previously (AOR=1.71;95% [CI] = 1.03, 2.83) or having prescribed injectable naltrexone previously (AOR=1.70;95% [CI] = 1.14, 2.56) compared to not prescribing MOUD previously;and working in an academic medical center (AOR=1.87;95% [CI] = 1.11, 3.14) compared to working in other clinical practice settings. CONCLUSIONS: Nearly a third of X-waivered buprenorphine prescribers supported provision of office-based methadone, specifically prescribers of Asian, Black, or African-American backgrounds, who had spent a longer time treating OUD, and had experience providing methadone. Future efforts should explore pathways to include office-based methadone to improve access to OUD treatment.
ABSTRACT
Background. The quantitative level of pathogens present in a host is a major driver of infectious disease (ID) state and outcome. However, the majority of ID diagnostics are qualitative. Next-generation sequencing (NGS) is an emerging ID diagnostics and research tool to provide insights, including tracking transmission, evolution, and identifying novel strains. Methods. We built a novel likelihood-based computational method to leverage pathogen-specific genome-wide NGS data to detect SARS-CoV-2, profile genetic variants, and furthermore quantify levels of these pathogens. We used de-identified clinical specimens tested for SARS-CoV-2 using RT-PCR, SARS-CoV-2 NGS Assay (hybrid capture, Twist Bioscience), or ARTIC (amplicon-based) platform, and COVID-DX software. A training (n=87) and validation (n=22) set was selected to establish the strength of our quantification model. We fit non-uniform probabilistic error profiles to a deterministic sigmoidal equation that more realistically represents observed data and used likelihood maximized over several different read depths to improve accuracy over a wide range of values of viral load. Given the proportion of the genome covered at varying depths for a single sample as input data, our model estimated the Ct of that sample as the value that produces the maximum likelihood of generating the observed genome coverage data. Results. The model fit on 87 SARS-CoV-2 NGS Assay training samples produced a good fit to the 22 validation samples, with a coefficient of correlation (r2) of ~0.8. The accuracy of the model was high (mean absolute % error of ~10%, meaning our model is able to predict the Ct value of each sample within a margin of ±10% on average). Because of the nature of the commonly used ARTIC protocol, we found that all quantitative signals in this data were lost during PCR amplification and the model is not applicable for quantification of samples captured this way. The ability to model quantification is a major advantage of the SARS-CoV-2 NGS assay protocol. Left. Observed genome coverage (y-axis) plotted against Ct value (x-axis). The best-fitting logistic curve is demonstrated with a red line with shaded areas above and below representing the fitted error profile. RIGHT: Model-estimated Ct values (y-axis) compared to laboratory Ct values (x-axis) with grey bars representing estimated confidence intervals. The 1:1 diagonal is shown as a dotted line. Conclusion. To our knowledge, this is the first model to incorporate sequence data mapped across the genome of a pathogen to quantify the level of that pathogen in a clinical specimen. This has implications in ID diagnostics, research, and metagenomics.