Machine learning takes a village: Assessing neighbourhood-level vulnerability for an overdose and infectious disease outbreak.

BACKGROUND: Multiple areas in the United States of America (USA) are experiencing high rates of overdose and outbreaks of bloodborne infections, including HIV and hepatitis C virus (HCV), due to non-sterile injection drug use. We aimed to identify neighbourhoods at increased vulnerability for overdose and infectious disease outbreaks in Rhode Island, USA. The primary aim was to pilot machine learning methods to identify which neighbourhood-level factors were important for creating "vulnerability assessment scores" across the state. The secondary aim was to engage stakeholders to pilot an interactive mapping tool and visualize the results. METHODS: From September 2018 to November 2019, we conducted a neighbourhood-level vulnerability assessment and stakeholder engagement process named The VILLAGE Project (Vulnerability Investigation of underlying Local risk And Geographic Events). We developed a predictive analytics model using machine learning methods (LASSO, Elastic Net, and RIDGE) to identify areas with increased vulnerability to an outbreak of overdose, HIV and HCV, using census tract-level counts of overdose deaths as a proxy for injection drug use patterns and related health outcomes. Stakeholders reviewed mapping tools for face validity and community distribution. RESULTS: Machine learning prediction models were suitable for estimating relative neighbourhood-level vulnerability to an outbreak. Variables of importance in the model included housing cost burden, prior overdose deaths, housing density, and education level. Eighty-nine census tracts (37%) with no prior overdose fatalities were identified as being vulnerable to such an outbreak, and nine of those were identified as having a vulnerability assessment score in the top 25%. Results were disseminated as a vulnerability stratification map and an online interactive mapping tool. CONCLUSION: Machine learning methods are well suited to predict neighborhoods at higher vulnerability to an outbreak. These methods show promise as a tool to assess structural vulnerabilities and work to prevent outbreaks at the local level.

Avoiding serotonin syndrome: the nature of the interaction between tramadol and selective serotonin reuptake inhibitors.

OBJECTIVE: To investigate the nature of the interaction between selective serotonin reuptake inhibitors (SSRIs) and tramadol to mitigate or avoid serotonin syndrome. DATA SOURCES: PubMed, Ovid MEDLINE, and International Pharmaceutical Abstracts from January 1990 to August 2012 were searched. Key words used were tramadol, antidepressive agents, antidepressants, drug interactions, selective serotonin uptake inhibitors, and serotonin syndrome. STUDY SELECTION AND DATA EXTRACTION: Only English-language studies were included. No randomized controlled trials were identified. Review articles, case reports, and 1 case series that identified the scope of interaction between tramadol and SSRIs were evaluated. Review articles evaluating the role of pharmacogenetics in the use of tramadol, SSRIs, and serotonin syndrome were also reviewed. DATA SYNTHESIS: Published documentation describing the interaction between tramadol and SSRIs and its relevance to serotonin syndrome is limited to a few case reports and 1 case series. While both tramadol and SSRIs increase the amount of serotonin in the brain, the interaction is much more complicated. Tramadol is metabolized through CYP2D6 enzymes and all SSRIs are inhibitors of these enzymes. Inhibitors of CYP2D6 can increase the concentration of tramadol in the blood and thus increase its effects on serotonin in the brain, contributing to the development of serotonin syndrome. CYP2D6 poor metabolizers are at a greater risk of serotonin syndrome and an inadequate analgesic effect. CONCLUSIONS: Coadministration of tramadol and SSRIs has caused serotonin syndrome. An attempt should be made to identify individuals who are poor metabolizers of CYP2D6 and avoid this combination in those patients. When SSRIs and tramadol must be used in combination, it is critical that patients be aware of the signs and symptoms of serotonin syndrome, should they occur.