Utility of Poison Control Center Data for Automated Opioid Overdose Surveillance.

CONTEXT: Overdosing on opioids is a national epidemic and the number one cause of death from unintentional injury in the United States. Poison control centers (PCCs) may be a source of timely data that can track opioid exposure cases, identify clusters of opioid exposure cases by geographic region, and capture opioid exposure cases that may not seek medical attention from health care facilities. OBJECTIVE: The objectives were to (a) identify data requirements for opioid overdose case ascertainment and classification and visualization in a dashboard, and (b) assess the availability and quality of the relevant PCC data for state-based opioid overdose surveillance. DESIGN: We identified types of opioid exposure, demographic characteristics, and other features that may be relevant for public health officials to monitor and respond to opioid overdose events in the community. We operationalized case definitions for an opioid overdose event based on the Centers for Disease Control and Prevention case classification definitions. We assessed the PCC database for concepts and metrics needed to operationalize case definitions for opioid overdose events to determine the feasibility of using the PCC for automated surveillance. MAIN OUTCOME MEASURE: Quality and availability of required concepts to operationalize metrics and case definitions using PCC data. RESULTS: A subset of the probable case definition may be used for automated surveillance with available structured PCC data. In contrast, logic for confirmed, suspected, and part of the probable case definitions requires additional structured data or analysis of narrative text, which may not contain needed concepts. For example, the confirmed case definition currently requires evidence from narrative text of laboratory confirmation of an opioid in a clinical specimen or diagnosis of opioid overdose in a health care record. CONCLUSION: PCC data are a timely and potentially useful source for automated surveillance of a subset of opioid overdose events, but additional structured and/or coded data are required.

Mining twitter to explore the emergence of COVID-19 symptoms.

BACKGROUND: The Centers for Disease Control and Prevention (CDC) in United States initially alerted the public to three COVID-19 signs and symptoms-fever, dry cough, and shortness of breath. Concurrent social media posts reflected a wider range of symptoms of COVID-19 besides these three symptoms. Because social media data have a potential application in the early identification novel virus symptoms, this study aimed to explore what symptoms mentioned in COVID-19-related social media posts during the early stages of the pandemic. METHODS: We collected COVID-19-related Twitter tweets posted in English language between March 30, 2020 and April 19, 2020 using search terms of COVID-19 synonyms and three common COVID-19 symptoms suggested by the CDC in March. Only unique tweets were extracted for analysis of symptom terms. RESULTS: A total of 36 symptoms were extracted from 30,732 unique tweets. All the symptoms suggested by the CDC for COVID-19 screening in March, April, and May were mentioned in tweets posted during the early stages of the pandemic. DISCUSSION: The findings of this study revealed that many COVID-19-related symptoms mentioned in Twitter tweets earlier than the announcement by the CDC. Monitoring social media data is a promising approach to public health surveillance.