This career development award will provide early career support for investigation of the management of infec- tious diseases in the setting of addiction in hospitals. The award will provide support for the candidate to develop expertise in the following areas: 1) addiction science research; 2) natural language processing; 3) machine learn- ing; 4) professional development; and 5) responsible conduct of research. For this, Dr. Goodman-Meza will be mentored by a multidisciplinary, cross-institutional team with expertise in addiction, infectious diseases, and data science. His primary mentor, Dr. Steve Shoptaw, has an extensive track record in addiction-related research and training of future independent investigators. His co-mentors include Dr. Alex Bui and Dr. Matthew B. Goetz. Dr. Bui is an expert in biomedical data science and heads NIH training programs in this field. Dr. Goetz has broad experience of productive infectious diseases clinical research within the Veterans Health Administration (VHA). The current opioid epidemic in the United States has been associated with an increase in infections, in particular hepatitis C and bacterial infections. Bacterial infections are the leading infectious diagnosis leading to hospitali- zation in individuals with an opioid use disorder (OUD), and incur significant healthcare expenditures. Despite the availability of opioid agonist therapy (OAT) in the form of methadone or buprenorphine, less than 20% of people with OUD actually receive OAT. Hospitalization for a bacterial infection may be an ideal time to initiate OAT, but the benefits of this practice are unknown. In this proposal, the candidate will assess the impact of initiating OAT in people who inject opioids admitted to the VHA due to a Staphylococcus aureus blood stream infection (bacteremia) ? the most common bacterial pathogen among people who inject opioids. Using data already collected for 36,868 cases of S. aureus bacteremia (SAB) from the VHA electronic data repository, the candidate will address three research questions: 1) is a natural language processing algorithm (NLP) more ac- curate than a standard International Classification of Diseases (ICD) code-based approach at screening records to correctly identify individuals who inject opioids in a cohort of patients admitted with SAB; 2) what are the temporal and geographic trends of SAB in people who inject opioids and those who receive OAT at the facility- level; and 3) using a machine learning framework, what are the estimated impacts of OAT on patient centered outcomes ? death, readmissions, leaving against medical advice, and subsequent outpatient engagement in OAT. These formative data will help the candidate to establish a productive early career as a physician-scientist and advise development of an OAT-delivery strategy to mitigate infectious complications of injection opioid use. Through this award, Dr. Goodman-Meza will establish himself as an expert physician-scientist at the intersection of infectious disease and addiction, poised to make significant contributions to this important area of medicine.
Serious, life-threatening bacterial infections in people who inject drugs are increasing with the current national opioid epidemic. Hospitalization of people who use opioids for treatment of infectious complications may be an ideal time to initiate opioid agonist therapy (OAT), an evidence-based practice that has been historically underuti- lized. This project will use innovative data science methods to estimate the impact of initiating OAT in the hospital for patients with a history of opioid injection admitted for treatment of blood stream infections caused by Staph- ylococcus aureus in the Veterans Health Administration.
