Is there a relationship between opioid use and transient global amnesia?

BACKGROUND AND PURPOSE: Opioid-associated amnestic syndrome (OAS) and transient global amnesia (TGA) are conditions with clinical overlap. We therefore sought to determine whether opioid use might be associated with TGA. METHODS: Data from the Massachusetts Department of Public Health Syndromic Surveillance program were queried to ascertain the frequency of opioid use among emergency department (ED) encounters for TGA compared to that for all other ED visits between January 2019 and June 2023. RESULTS: A total of 13,188,630 ED visits were identified during the study period. Of 1417 visits for TGA, one visit met the exposure definition for opioid use. There were 13,187,213 visits for other indications, 57,638 of which were considered opioid-exposed. The odds ratio for the relationship between opioid use and TGA was 0.16 (95% confidence interval 0.02, 1.14). CONCLUSION: Despite the clinical overlap between OAS and TGA, surveillance data from ED visits in Massachusetts do not suggest that opioid use is a risk factor for TGA, indicating that OAS and TGA are distinct entities.

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States.

Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.

Application of an Existing Syndromic Surveillance System to Quantify Possible Cases of Opioid-associated Amnestic Syndrome in Massachusetts.

OBJECTIVES: In recent years, an opioid-associated amnestic syndrome (OAS) was identified in Massachusetts through elicited reporting by health care providers (traditional surveillance, TS). Whether OAS occurs more frequently and with a wider spatiotemporal distribution in Massachusetts remains unclear. We compared the frequency and spatiotemporal characteristics of emergency department (ED) visits for possible OAS (pOAS) using a pre-existing syndromic surveillance system (SyS) with OAS cases captured through TS. METHODS: SyS was queried for Massachusetts ED visits in 15- to 55- year-olds with a chief complaint text and discharge codes for memory loss in association with codes for opioid use (pOAS). SyS data were extracted for 2016-2020, whereas TS was conducted for 2012-2018. Cases identified by SyS and TS were stratified by demographic and spatiotemporal variables. RESULTS: TS ascertained 22 reported cases of OAS (18 males) between 2012 and 2018, ranging from 0 to 5 annually. No identified OAS patients presented between January and March or in western Massachusetts. Between 2016 and 2020, SyS identified 82 ED visits (49 males) with pOAS, ranging from 13 to 22 per year. Over the 5-year period, at least 2 ED visits for pOAS occurred during each month of the year (24 total during January, February, or March) and at least 1 visit occurred in each county except 2, with the second largest number (11) in Berkshire County (at the western border of Massachusetts), where no cases were ascertained through TS. CONCLUSIONS: Although OAS is a relatively rare condition, use of SyS in Massachusetts suggests a broader and more frequent spatiotemporal distribution than previously indicated from TS.

Linking Ambulance Trip and Emergency Department Surveillance Data on Opioid-Related Overdose, Massachusetts, 2017.

OBJECTIVES: Studies describing linkage of ambulance trips and emergency department (ED) visits of patients with opioid-related overdose (ORO) are limited. We linked records of patients experiencing ORO from ambulance trip and ED visit records in Massachusetts during April 1-June 30, 2017. METHODS: We estimated the positive predictive value of ORO-capturing definitions by examining the narratives and triage notes of a sample of OROs from each data source. Because of a lack of common unique identifiers, we deterministically linked OROs to records in the counter data set on date of birth, incident date, facility, and sex. To validate the linkage strategy, we compared ambulance trip narratives with ED triage notes and chief complaints for a sample of pairs. RESULTS: Of 3203 ambulance trips for ORO and 3046 ED visits for ORO, 82% and 63%, respectively, matched a record in the counter data set on date of birth, incident date, facility, and sex. In 200 randomly selected linked pairs from a final linked data set of 3006 paired records, only 5 (3%) appeared to be false matches. PRACTICE IMPLICATIONS: This exercise demonstrated the feasibility of linking ORO records between 2 data sets without a unique identifier. Future analyses of the linked data could produce insights not available from analyzing either data set alone. Linkage using 2 rapidly available data sets can actively inform the state's public health opioid overdose response and allow for de-duplicating counts of OROs treated by ambulance, in an ED, or both.

The impact of the national stay-at-home order on emergency department visits for suspected opioid overdose during the first wave of the COVID-19 pandemic.

BACKGROUND: Although national syndromic surveillance data reported declines in emergency department (ED) visits after the declaration of the national stay-at-home order for COVID-19, little is known whether these declines were observed for suspected opioid overdose. METHODS: This interrupted time series study used syndromic surveillance data from four states participating in the HEALing Communities Study: Kentucky, Massachusetts, New York, and Ohio. All ED encounters for suspected opioid overdose (n = 48,301) occurring during the first 31 weeks of 2020 were included. We examined the impact of the national public health emergency for COVID-19 (declared on March 14, 2020) on trends in ED encounters for suspected opioid overdose. RESULTS: Three of four states (Massachusetts, New York and Ohio) experienced a statistically significant immediate decline in the rate of ED encounters for suspected opioid overdose (per 100,000) after the nationwide public health emergency declaration (MA: -0.99; 95 % CI: -1.75, -0.24; NY: -0.10; 95 % CI, -0.20, 0.0; OH: -0.33, 95 % CI: -0.58, -0.07). After this date, Ohio and Kentucky experienced a sustained rate of increase for a 13-week period. New York experienced a decrease in the rate of ED encounters for a 10-week period, after which the rate began to increase. In Massachusetts after a significant immediate decline in the rate of ED encounters, there was no significant difference in the rate of change for a 6-week period, followed by an immediate increase in the ED rate to higher than pre-COVID levels. CONCLUSIONS: The heterogeneity in the trends in ED encounters between the four sites show that the national stay-at-home order had a differential impact on opioid overdose ED presentation in each state.

Drug repositioning candidates identified using in-silico quasi-quantum molecular simulation demonstrate reduced COVID-19 mortality in 1.5M patient records.

Background: Drug repositioning is a key component of COVID-19 pandemic response, through identification of existing drugs that can effectively disrupt COVID-19 disease processes, contributing valuable insights into disease pathways. Traditional non in silico drug repositioning approaches take substantial time and cost to discover effect and, crucially, to validate repositioned effects. Methods: Using a novel in-silico quasi-quantum molecular simulation platform that analyzes energies and electron densities of both target proteins and candidate interruption compounds on High Performance Computing (HPC), we identified a list of FDA-approved compounds with potential to interrupt specific SARS-CoV-2 proteins. Subsequently we used 1.5M patient records from the National COVID Cohort Collaborative to create matched cohorts to refine our in-silico hits to those candidates that show statistically significant clinical effect. Results: We identified four drugs, Metformin, Triamcinolone, Amoxicillin and Hydrochlorothiazide, that were associated with reduced mortality by 27%, 26%, 26%, and 23%, respectively, in COVID-19 patients. Conclusions: Together, these findings provide support to our hypothesis that in-silico simulation of active compounds against SARS-CoV-2 proteins followed by statistical analysis of electronic health data results in effective therapeutics identification.

Antimicrobial resistance surveillance in the AFHSC-GEIS network.

International infectious disease surveillance has been conducted by the United States (U.S.) Department of Defense (DoD) for many years and has been consolidated within the Armed Forces Health Surveillance Center, Division of Global Emerging Infections Surveillance and Response System (AFHSC-GEIS) since 1998. This includes activities that monitor the presence of antimicrobial resistance among pathogens. AFHSC-GEIS partners work within DoD military treatment facilities and collaborate with host-nation civilian and military clinics, hospitals and university systems. The goals of these activities are to foster military force health protection and medical diplomacy. Surveillance activities include both community-acquired and health care-associated infections and have promoted the development of surveillance networks, centers of excellence and referral laboratories. Information technology applications have been utilized increasingly to aid in DoD-wide global surveillance for diseases significant to force health protection and global public health. This section documents the accomplishments and activities of the network through AFHSC-GEIS partners in 2009.