Adequacy of Existing Surveillance Systems to Monitor Racism, Social Stigma and COVID Inequities: A Detailed Assessment and Recommendations.

The populations impacted most by COVID are also impacted by racism and related social stigma; however, traditional surveillance tools may not capture the intersectionality of these relationships. We conducted a detailed assessment of diverse surveillance systems and databases to identify characteristics, constraints and best practices that might inform the development of a novel COVID surveillance system that achieves these aims. We used subject area expertise, an expert panel and CDC guidance to generate an initial list of N > 50 existing surveillance systems as of 29 October 2020, and systematically excluded those not advancing the project aims. This yielded a final reduced group (n = 10) of COVID surveillance systems (n = 3), other public health systems (4) and systems tracking racism and/or social stigma (n = 3, which we evaluated by using CDC evaluation criteria and Critical Race Theory. Overall, the most important contribution of COVID-19 surveillance systems is their real-time (e.g., daily) or near-real-time (e.g., weekly) reporting; however, they are severely constrained by the lack of complete data on race/ethnicity, making it difficult to monitor racial/ethnic inequities. Other public health systems have validated measures of psychosocial and behavioral factors and some racism or stigma-related factors but lack the timeliness needed in a pandemic. Systems that monitor racism report historical data on, for instance, hate crimes, but do not capture current patterns, and it is unclear how representativeness the findings are. Though existing surveillance systems offer important strengths for monitoring health conditions or racism and related stigma, new surveillance strategies are needed to monitor their intersecting relationships more rigorously.

Leveraging Social Media Activity and Machine Learning for HIV and Substance Abuse Risk Assessment: Development and Validation Study.

BACKGROUND: Social media networks provide an abundance of diverse information that can be leveraged for data-driven applications across various social and physical sciences. One opportunity to utilize such data exists in the public health domain, where data collection is often constrained by organizational funding and limited user adoption. Furthermore, the efficacy of health interventions is often based on self-reported data, which are not always reliable. Health-promotion strategies for communities facing multiple vulnerabilities, such as men who have sex with men, can benefit from an automated system that not only determines health behavior risk but also suggests appropriate intervention targets. OBJECTIVE: This study aims to determine the value of leveraging social media messages to identify health risk behavior for men who have sex with men. METHODS: The Gay Social Networking Analysis Program was created as a preliminary framework for intelligent web-based health-promotion intervention. The program consisted of a data collection system that automatically gathered social media data, health questionnaires, and clinical results for sexually transmitted diseases and drug tests across 51 participants over 3 months. Machine learning techniques were utilized to assess the relationship between social media messages and participants' offline sexual health and substance use biological outcomes. The F1 score, a weighted average of precision and recall, was used to evaluate each algorithm. Natural language processing techniques were employed to create health behavior risk scores from participant messages. RESULTS: Offline HIV, amphetamine, and methamphetamine use were correctly identified using only social media data, with machine learning models obtaining F1 scores of 82.6%, 85.9%, and 85.3%, respectively. Additionally, constructed risk scores were found to be reasonably comparable to risk scores adapted from the Center for Disease Control. CONCLUSIONS: To our knowledge, our study is the first empirical evaluation of a social media-based public health intervention framework for men who have sex with men. We found that social media data were correlated with offline sexual health and substance use, verified through biological testing. The proof of concept and initial results validate that public health interventions can indeed use social media-based systems to successfully determine offline health risk behaviors. The findings demonstrate the promise of deploying a social media-based just-in-time adaptive intervention to target substance use and HIV risk behavior.

Association of prescription opioid use on mortality and hospital length of stay in the intensive care unit.

OBJECTIVE: Several studies show that chronic opioid dependence leads to higher in-hospital mortality, increased risk of hospital readmissions, and worse outcomes in trauma cases. However, the association of outpatient prescription opioid use on morbidity and mortality has not been adequately evaluated in a critical care setting. The purpose of this study was to determine if there is an association between chronic opioid use and mortality after an ICU admission. DESIGN: A single-center, longitudinal retrospective cohort study of all Intensive Care Unit (ICU) patients admitted to a tertiary-care academic medical center from 2001 to 2012 using the MIMIC-III database. SETTING: Medical Information Mart for Intensive Care III database based in the United States. PATIENTS: Adult patients 18 years and older were included. Exclusion criteria comprised of patients who expired during their hospital stay or presented with overdose; patients with cancer, anoxic brain injury, non-prescription opioid use; or if an accurate medication reconciliation was unable to be obtained. Patients prescribed chronic opioids were compared with those who had not been prescribed opioids in the outpatient setting. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The final sample included a total of 22,385 patients, with 2,621 (11.7%) in the opioid group and 19,764 (88.3%) in the control group. After proceeding with bivariate analyses, statistically significant and clinically relevant differences were identified between opioid and non-opioid users in sex, length of hospital stay, and comorbidities. Opioid use was associated with increased mortality in both the 30-day and 1-year windows with a respective odds ratios of 1.81 (95% CI, 1.63-2.01; p<0.001) and 1.88 (95% CI, 1.77-1.99; p<0.001), respectively. CONCLUSIONS: Chronic opioid usage was associated with increased hospital length of stay and increased mortality at both 30 days and 1 year after ICU admission. Knowledge of this will help providers make better choices in patient care and have a more informed risk-benefits discussion when prescribing opioids for chronic usage.

Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing.

Poor medication adherence threatens an individual's health and is responsible for substantial medical costs in the United States annually. In order to improve medication adherence rates and provide timely reminders, we developed a smartwatch application that collects data from embedded inertial sensors, which include an accelerometer and gyroscope, to monitor a series of actions happening during an individual's medication intake. After the collected data was delivered to a server, Apache Spark was used to distribute the data and apply machine learning algorithms in order to predict several discrete actions including medication intake. By utilizing these tools, we were able to preprocess high frequency sensor data and apply a random forest algorithm, yielding high frequency and recall of the aforementioned actions.