ABSTRACT
Co-circulation of respiratory viruses compounded by similarities in clinical presentation and mode of transmission underscores the need for broad range pathogen detection. Accurate identification and diagnosis at the point-of-need is critical to limiting disease spread. A novel point-of-need Raman spectroscopy-based platform is described for rapid detection of multiple respiratory pathogens in nasal swab samples with high sensitivity and specificity. The system takes advantage of a counter-propagating Gaussian beam focused within the sample chamber that augments the Raman signal of pathogens. Combined with multiclass machine learning spectral analysis via Gradient Boosting Machine, accurate identification of SARS-CoV-2, human coronaviruses OC43, NL63, 229E, Influenza A (H1N1), respiratory syncytial virus, and Streptococcus pyogenes in spiked clinical nasal swab samples was demonstrated at 99% sensitivity and 93% specificity. The limit of detection was assessed using binary class Support Vector Machine with SARS-CoV-2 in nasal swab samples against negative control at 2.2 × 104 virions/swab. The spectrometer can be operated by minimally trained personnel with software-generated diagnostic yes/no results in 2 min or less, making it well suited for point-of-need applications. Furthermore, adaptive algorithms can detect and differentiate new and emerging variants using a Raman spectral database.
ABSTRACT
This article describes our experience developing a novel mobile health unit (MHU) program in the Detroit, Michigan, metropolitan area. Our main objectives were to improve healthcare accessibility, quality and equity in our community during the novel coronavirus pandemic. While initially focused on SARS-CoV-2 testing, our program quickly evolved to include preventive health services. The MHU program began as a location-based SARS-CoV-2 testing strategy coordinated with local and state public health agencies. Community needs motivated further program expansion to include additional preventive healthcare and social services. MHU deployment was targeted to disease "hotspots" based on publicly available SARS-CoV-2 testing data and community-level information about social vulnerability. This formative evaluation explores whether our MHU deployment strategy enabled us to reach patients from communities with heightened social vulnerability as intended. From 3/20/20-3/24/21, the Detroit MHU program reached a total of 32,523 people. The proportion of patients who resided in communities with top quartile Centers for Disease Control and Prevention Social Vulnerability Index rankings increased from 25% during location-based "drive-through" SARS-CoV-2 testing (3/20/20-4/13/20) to 27% after pivoting to a mobile platform (4/13/20-to-8/31/20; p = 0.01). The adoption of a data-driven deployment strategy resulted in further improvement; 41% of the patients who sought MHU services from 9/1/20-to-3/24/21 lived in vulnerable communities (Cochrane Armitage test for trend, p<0.001). Since 10/1/21, 1,837 people received social service referrals and, as of 3/15/21, 4,603 were administered at least one dose of COVID-19 vaccine. Our MHU program demonstrates the capacity to provide needed healthcare and social services to difficult-to-reach populations from areas with heightened social vulnerability. This model can be expanded to meet emerging pandemic needs, but it is also uniquely capable of improving health equity by addressing longstanding gaps in primary care and social services in vulnerable communities.