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1.
Int J Environ Res Public Health ; 19(8)2022 04 13.
Article in English | MEDLINE | ID: covidwho-1809866

ABSTRACT

Syndromic surveillance involves the near-real-time collection of data from a potential multitude of sources to detect outbreaks of disease or adverse health events earlier than traditional forms of public health surveillance. The purpose of the present study is to elucidate the role of syndromic surveillance during mass gathering scenarios. In the present review, the use of syndromic surveillance for mass gathering scenarios is described, including characteristics such as methodologies of data collection and analysis, degree of preparation and collaboration, and the degree to which prior surveillance infrastructure is utilized. Nineteen publications were included for data extraction. The most common data source for the included syndromic surveillance systems was emergency departments, with first aid stations and event-based clinics also present. Data were often collected using custom reporting forms. While syndromic surveillance can potentially serve as a method of informing public health policy regarding specific mass gatherings based on the profile of syndromes ascertained, the present review does not indicate that this form of surveillance is a reliable method of detecting potentially critical public health events during mass gathering scenarios.


Subject(s)
Sentinel Surveillance , Disease Outbreaks , Emergency Service, Hospital , Population Surveillance , Public Health Surveillance/methods
2.
Int J Environ Res Public Health ; 17(10)2020 05 14.
Article in English | MEDLINE | ID: covidwho-1725617

ABSTRACT

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.


Subject(s)
Artificial Intelligence , Coronavirus Infections/prevention & control , Coronavirus , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , Public Health Surveillance/methods , Betacoronavirus , COVID-19 , Coronavirus Infections/epidemiology , Disease Outbreaks , Humans , Pneumonia, Viral/epidemiology , Public Health , Public Health Informatics , SARS-CoV-2
3.
JMIR Public Health Surveill ; 8(2): e28737, 2022 02 24.
Article in English | MEDLINE | ID: covidwho-1702861

ABSTRACT

BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the "Delta wave" appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. OBJECTIVE: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. METHODS: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. RESULTS: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami's speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. CONCLUSIONS: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , Humans , Longitudinal Studies , Pandemics/prevention & control , Public Health Surveillance/methods , SARS-CoV-2
4.
MMWR Morb Mortal Wkly Rep ; 71(6): 206-211, 2022 02 11.
Article in English | MEDLINE | ID: covidwho-1687588

ABSTRACT

Genomic surveillance is a critical tool for tracking emerging variants of SARS-CoV-2 (the virus that causes COVID-19), which can exhibit characteristics that potentially affect public health and clinical interventions, including increased transmissibility, illness severity, and capacity for immune escape. During June 2021-January 2022, CDC expanded genomic surveillance data sources to incorporate sequence data from public repositories to produce weighted estimates of variant proportions at the jurisdiction level and refined analytic methods to enhance the timeliness and accuracy of national and regional variant proportion estimates. These changes also allowed for more comprehensive variant proportion estimation at the jurisdictional level (i.e., U.S. state, district, territory, and freely associated state). The data in this report are a summary of findings of recent proportions of circulating variants that are updated weekly on CDC's COVID Data Tracker website to enable timely public health action.† The SARS-CoV-2 Delta (B.1.617.2 and AY sublineages) variant rose from 1% to >50% of viral lineages circulating nationally during 8 weeks, from May 1-June 26, 2021. Delta-associated infections remained predominant until being rapidly overtaken by infections associated with the Omicron (B.1.1.529 and BA sublineages) variant in December 2021, when Omicron increased from 1% to >50% of circulating viral lineages during a 2-week period. As of the week ending January 22, 2022, Omicron was estimated to account for 99.2% (95% CI = 99.0%-99.5%) of SARS-CoV-2 infections nationwide, and Delta for 0.7% (95% CI = 0.5%-1.0%). The dynamic landscape of SARS-CoV-2 variants in 2021, including Delta- and Omicron-driven resurgences of SARS-CoV-2 transmission across the United States, underscores the importance of robust genomic surveillance efforts to inform public health planning and practice.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , SARS-CoV-2/genetics , Centers for Disease Control and Prevention, U.S. , Genomics , Humans , Prevalence , Public Health Surveillance/methods , United States/epidemiology
6.
Public Health Rep ; 137(2): 239-243, 2022.
Article in English | MEDLINE | ID: covidwho-1673687

ABSTRACT

Monitoring COVID-19 vaccination coverage among nursing home residents and staff is important to ensure high coverage rates and guide patient-safety policies. With the termination of the federal Pharmacy Partnership for Long-Term Care Program, another source of facility-based vaccination data is needed. We compared numbers of COVID-19 vaccinations administered to nursing home residents and staff reported by pharmacies participating in the temporary federal Pharmacy Partnership for Long-Term Care Program with the numbers of COVID-19 vaccinations reported by nursing homes participating in new COVID-19 vaccination modules of the Centers for Disease Control and Prevention's National Healthcare Safety Network (NHSN). Pearson correlation coefficients comparing the number vaccinated between the 2 approaches were 0.89, 0.96, and 0.97 for residents and 0.74, 0.90, and 0.90 for staff, in the weeks ending January 3, 10, and 17, 2021, respectively. Based on subsequent NHSN reporting, vaccination coverage with ≥1 vaccine dose reached 73.7% for residents and 47.6% for staff the week ending January 31 and increased incrementally through July 2021. Continued monitoring of COVID-19 vaccination coverage is important as new nursing home residents are admitted, new staff are hired, and additional doses of vaccine are recommended.


Subject(s)
COVID-19/prevention & control , Long-Term Care , Nursing Homes , Vaccination Coverage/statistics & numerical data , Centers for Medicare and Medicaid Services, U.S. , Humans , Mandatory Reporting , Public Health Surveillance/methods , SARS-CoV-2 , United States
7.
Brief Bioinform ; 23(2)2022 03 10.
Article in English | MEDLINE | ID: covidwho-1639367

ABSTRACT

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Public Health Surveillance/methods , SARS-CoV-2/genetics , Software , Web Browser , Computational Biology/methods , DNA Mutational Analysis , Databases, Genetic , Genome, Viral , Genomics , Humans , Molecular Epidemiology/methods , Molecular Sequence Annotation , Mutation
8.
Lancet Glob Health ; 10(2): e269-e277, 2022 02.
Article in English | MEDLINE | ID: covidwho-1625222

ABSTRACT

BACKGROUND: Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory tract infections and a key driver of childhood mortality. Previous RSV burden of disease estimates used hospital-based surveillance data and modelled, rather than directly measured, community deaths. Given this uncertainty, we conducted a 3-year post-mortem prevalence study among young infants at a busy morgue in Lusaka, Zambia-the Zambia Pertussis RSV Infant Mortality Estimation (ZPRIME) study. METHODS: Infants were eligible for inclusion if they were aged between 4 days and less than 6 months and were enrolled within 48 h of death. Enrolment occurred mainly at the University Teaching Hospital of the University of Zambia Medical School (Lusaka, Zambia), the largest teaching hospital in Zambia. We extracted demographic and clinical data from medical charts and official death certificates, and we conducted verbal autopsies with the guardian or next of kin. RSV was identified using reverse transcriptase quantitative PCR and stratified by age, time of year, and setting (community vs facility deaths). By combining the PCR prevalence data with syndromic presentation, we estimated the proportion of all infant deaths that were due to RSV. FINDINGS: The ZPRIME study ran from Aug 31, 2017, to Aug 31, 2020, except for from April 1 to May 6, 2020, during which data were not collected due to restrictions on human research at this time (linked to COVID-19). We enrolled 2286 deceased infants, representing 79% of total infant deaths in Lusaka. RSV was detected in 162 (7%) of 2286 deceased infants. RSV was detected in 102 (9%) of 1176 community deaths, compared with 10 (4%) of 236 early facility deaths (<48 h from admission) and 36 (5%) of 737 late facility deaths (≥48 h from admission). RSV deaths were concentrated in infants younger than 3 months (116 [72%] of 162 infants), and were clustered in the first half of each year and in the poorest and most densely populated Lusaka townships. RSV caused at least 2·8% (95% CI 1·0-4·6) of all infant deaths and 4·7% (1·3-8·1) of community deaths. INTERPRETATION: RSV was a major seasonal cause of overall infant mortality, particularly among infants younger than 3 months of age. Because most RSV deaths occurred in the community and would have been missed through hospital-based surveillance, the global burden of fatal RSV has probably been underestimated. FUNDING: Bill & Melinda Gates Foundation.


Subject(s)
Respiratory Syncytial Virus Infections/mortality , Female , Humans , Infant , Infant, Newborn , Male , Public Health Surveillance/methods , Respiratory Syncytial Virus Infections/diagnosis , Respiratory Syncytial Virus Infections/drug therapy , Respiratory Syncytial Virus, Human , Reverse Transcriptase Polymerase Chain Reaction , Zambia/epidemiology
10.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569348

ABSTRACT

Simultaneously tracking the global impact of COVID-19 is challenging because of regional variation in resources and reporting. Leveraging self-reported survey outcomes via an existing international social media network has the potential to provide standardized data streams to support monitoring and decision-making worldwide, in real time, and with limited local resources. The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS), in partnership with Facebook, has invited daily cross-sectional samples from the social media platform's active users to participate in the survey since its launch on April 23, 2020. We analyzed UMD-CTIS survey data through December 20, 2020, from 31,142,582 responses representing 114 countries/territories weighted for nonresponse and adjusted to basic demographics. We show consistent respondent demographics over time for many countries/territories. Machine Learning models trained on national and pooled global data verified known symptom indicators. COVID-like illness (CLI) signals were correlated with government benchmark data. Importantly, the best benchmarked UMD-CTIS signal uses a single survey item whereby respondents report on CLI in their local community. In regions with strained health infrastructure but active social media users, we show it is possible to define COVID-19 impact trajectories using a remote platform independent of local government resources. This syndromic surveillance public health tool is the largest global health survey to date and, with brief participant engagement, can provide meaningful, timely insights into the global COVID-19 pandemic at a local scale.


Subject(s)
COVID-19/epidemiology , Public Health Surveillance/methods , Social Media , COVID-19/diagnosis , COVID-19 Testing , Cross-Sectional Studies , Epidemiologic Methods , Humans , Internationality , Machine Learning , Pandemics/statistics & numerical data
11.
Am J Public Health ; 111(12): 2133-2140, 2021 12.
Article in English | MEDLINE | ID: covidwho-1562412

ABSTRACT

The National Center for Health Statistics' (NCHS's) National Vital Statistics System (NVSS) collects, processes, codes, and reviews death certificate data and disseminates the data in annual data files and reports. With the global rise of COVID-19 in early 2020, the NCHS mobilized to rapidly respond to the growing need for reliable, accurate, and complete real-time data on COVID-19 deaths. Within weeks of the first reported US cases, NCHS developed certification guidance, adjusted internal data processing systems, and stood up a surveillance system to release daily updates of COVID-19 deaths to track the impact of the COVID-19 pandemic on US mortality. This report describes the processes that NCHS took to produce timely mortality data in response to the COVID-19 pandemic. (Am J Public Health. 2021;111(12):2133-2140. https://doi.org/10.2105/AJPH.2021.306519).


Subject(s)
COVID-19/mortality , Data Collection/standards , Public Health Surveillance/methods , Vital Statistics , Cause of Death , Clinical Coding/standards , Guidelines as Topic , Health Status Disparities , Humans , SARS-CoV-2 , Time Factors , United States/epidemiology
12.
Am J Public Health ; 111(12): 2127-2132, 2021 12.
Article in English | MEDLINE | ID: covidwho-1561284

ABSTRACT

More than a year after the first domestic COVID-19 cases, the United States does not have national standards for COVID-19 surveillance data analysis and public reporting. This has led to dramatic variations in surveillance practices among public health agencies, which analyze and present newly confirmed cases by a wide variety of dates. The choice of which date to use should be guided by a balance between interpretability and epidemiological relevance. Report date is easily interpretable, generally representative of outbreak trends, and available in surveillance data sets. These features make it a preferred date for public reporting and visualization of surveillance data, although it is not appropriate for epidemiological analyses of outbreak dynamics. Symptom onset date is better suited for such analyses because of its clinical and epidemiological relevance. However, using symptom onset for public reporting of new confirmed cases can cause confusion because reporting lags result in an artificial decline in recent cases. We hope this discussion is a starting point toward a more standardized approach to date-based surveillance. Such standardization could improve public comprehension, policymaking, and outbreak response. (Am J Public Health. 2021;111(12):2127-2132. https://doi.org/10.2105/AJPH.2021.306520).


Subject(s)
COVID-19/epidemiology , Data Collection/methods , Data Collection/standards , Public Health Surveillance/methods , Centers for Disease Control and Prevention, U.S./standards , Guidelines as Topic , Humans , SARS-CoV-2 , Time Factors , United States/epidemiology
15.
JMIR Public Health Surveill ; 8(2): e28737, 2022 02 24.
Article in English | MEDLINE | ID: covidwho-1560174

ABSTRACT

BACKGROUND: Despite the availability of vaccines, the US incidence of new COVID-19 cases per day nearly doubled from the beginning of July to the end of August 2021, fueled largely by the rapid spread of the Delta variant. While the "Delta wave" appears to have peaked nationally, some states and municipalities continue to see elevated numbers of new cases. Vigilant surveillance including at a metropolitan level can help identify any reignition and validate continued and strong public health policy responses in problem localities. OBJECTIVE: This surveillance report aimed to provide up-to-date information for the 25 largest US metropolitan areas about the rapidity of descent in the number of new cases following the Delta wave peak, as well as any potential reignition of the pandemic associated with declining vaccine effectiveness over time, new variants, or other factors. METHODS: COVID-19 pandemic dynamics for the 25 largest US metropolitan areas were analyzed through September 19, 2021, using novel metrics of speed, acceleration, jerk, and 7-day persistence, calculated from the observed data on the cumulative number of cases as reported by USAFacts. Statistical analysis was conducted using dynamic panel data models estimated with the Arellano-Bond regression techniques. The results are presented in tabular and graphic forms for visual interpretation. RESULTS: On average, speed in the 25 largest US metropolitan areas declined from 34 new cases per day per 100,000 population, during the week ending August 15, 2021, to 29 new cases per day per 100,000 population, during the week ending September 19, 2021. This average masks important differences across metropolitan areas. For example, Miami's speed decreased from 105 for the week ending August 15, 2021, to 40 for the week ending September 19, 2021. Los Angeles, San Francisco, Riverside, and San Diego had decreasing speed over the sample period and ended with single-digit speeds for the week ending September 19, 2021. However, Boston, Washington DC, Detroit, Minneapolis, Denver, and Charlotte all had their highest speed of the sample during the week ending September 19, 2021. These cities, as well as Houston and Baltimore, had positive acceleration for the week ending September 19, 2021. CONCLUSIONS: There is great variation in epidemiological curves across US metropolitan areas, including increasing numbers of new cases in 8 of the largest 25 metropolitan areas for the week ending September 19, 2021. These trends, including the possibility of waning vaccine effectiveness and the emergence of resistant variants, strongly indicate the need for continued surveillance and perhaps a return to more restrictive public health guidelines for some areas.


Subject(s)
COVID-19 , Pandemics , COVID-19/epidemiology , Humans , Longitudinal Studies , Pandemics/prevention & control , Public Health Surveillance/methods , SARS-CoV-2
17.
Am J Public Health ; 111(12): 2186-2193, 2021 12.
Article in English | MEDLINE | ID: covidwho-1559987

ABSTRACT

The purpose of this analytic essay is to contrast the COVID-19 responses in Cuba and the United States, and to understand the differences in outcomes between the 2 nations. With fundamental differences in health systems structure and organization, as well as in political philosophy and culture, it is not surprising that there are major differences in outcomes. The more coordinated, comprehensive response to COVID-19 in Cuba has resulted in significantly better outcomes compared with the United States. Through July 15, 2021, the US cumulative case rate is more than 4 times higher than Cuba's, while the death rate and excess death rate are both approximately 12 times higher in the United States. In addition to the large differences in cumulative case and death rates between United States and Cuba, the COVID-19 pandemic has unmasked serious underlying health inequities in the United States. The vaccine rollout presents its own set of challenges for both countries, and future studies can examine the comparative successes to identify effective strategies for distribution and administration. (Am J Public Health. 2021;111(12):2186-2193. https://doi.org/10.2105/AJPH.2021.306526).


Subject(s)
COVID-19/epidemiology , COVID-19/mortality , COVID-19/prevention & control , COVID-19 Vaccines/administration & dosage , Communicable Disease Control/organization & administration , Cuba/epidemiology , Humans , Pandemics , Public Health Surveillance/methods , SARS-CoV-2 , United States/epidemiology
19.
Arch Dis Child ; 106(11): 1050-1055, 2021 11.
Article in English | MEDLINE | ID: covidwho-1501685

ABSTRACT

BACKGROUND: Globally, injuries cause >5 million deaths annually and children and young people are particularly vulnerable. Injuries are the leading cause of death in people aged 5-24 years and a leading cause of disability. In most low-income and middle-income countries where the majority of global child injury burden occurs, systems for routinely collecting injury data are limited. METHODS: A new model of injury surveillance for use in emergency departments in Nepal was designed and piloted. Data from patients presenting with injuries were collected prospectively over 12 months and used to describe the epidemiology of paediatric injury presentations. RESULTS: The total number of children <18 years of age presenting with injury was 2696, representing 27% of all patients presenting with injuries enrolled. Most injuries in children presenting to the emergency departments in this study were unintentional and over half of children were <10 years of age. Falls, animal bites/stings and road traffic injuries accounted for nearly 75% of all injuries with poisonings, burns and drownings presenting proportionately less often. Over half of injuries were cuts, bites and open wounds. In-hospital child mortality from injury was 1%. CONCLUSION: Injuries affecting children in Nepal represent a significant burden. The data on injuries observed from falls, road traffic injuries and injuries related to animals suggest potential areas for injury prevention. This is the biggest prospective injury surveillance study in Nepal in recent years and supports the case for using injury surveillance to monitor child morbidity and mortality through improved data.


Subject(s)
Emergency Service, Hospital/statistics & numerical data , Global Burden of Disease/economics , Public Health Surveillance/methods , Wounds and Injuries/epidemiology , Accidental Falls/statistics & numerical data , Accidents, Traffic/statistics & numerical data , Adolescent , Animals , Bites and Stings/epidemiology , Burns/epidemiology , Child , Child, Preschool , Drowning/epidemiology , Emergency Service, Hospital/trends , Female , Humans , Male , Nepal/epidemiology , Poisoning/epidemiology , Prospective Studies , Trauma Severity Indices , Wounds and Injuries/mortality , Wounds and Injuries/prevention & control
20.
Public Health Rep ; 136(1_suppl): 72S-79S, 2021.
Article in English | MEDLINE | ID: covidwho-1495836

ABSTRACT

OBJECTIVE: Traditional public health surveillance of nonfatal opioid overdose relies on emergency department (ED) billing data, which can be delayed substantially. We compared the timeliness of 2 new data sources for rapid drug overdose surveillance-emergency medical services (EMS) and syndromic surveillance-with ED billing data. METHODS: We used data on nonfatal opioid overdoses in Kentucky captured in EMS, syndromic surveillance, and ED billing systems during 2018-2019. We evaluated the time-series relationships between EMS and ED billing data and syndromic surveillance and ED billing data by calculating cross-correlation functions, controlling for influences of autocorrelations. A case example demonstrates the usefulness of EMS and syndromic surveillance data to monitor rapid changes in opioid overdose encounters in Kentucky during the COVID-19 epidemic. RESULTS: EMS and syndromic surveillance data showed moderate-to-strong correlation with ED billing data on a lag of 0 (r = 0.694; 95% CI, 0.579-0.782; t = 9.73; df = 101; P < .001; and r = 0.656; 95% CI, 0.530-0.754; t = 8.73; df = 101; P < .001; respectively) at the week-aggregated level. After the COVID-19 emergency declaration, EMS and syndromic surveillance time series had steep increases in April and May 2020, followed by declines from June through September 2020. The ED billing data were available for analysis 3 months after the end of a calendar quarter but closely followed the trends identified by the EMS and syndromic surveillance data. CONCLUSION: Data from EMS and syndromic surveillance systems can be reliably used to monitor nonfatal opioid overdose trends in Kentucky in near-real time to inform timely public health response.


Subject(s)
Analgesics, Opioid/poisoning , Drug Overdose/epidemiology , Emergency Medical Services/statistics & numerical data , Opioid-Related Disorders/epidemiology , Population Surveillance/methods , Public Health Surveillance/methods , Sentinel Surveillance , Analgesics, Opioid/administration & dosage , COVID-19/epidemiology , Drug Overdose/prevention & control , Emergencies/epidemiology , Emergency Medical Services/trends , Humans , Kentucky/epidemiology , Pandemics , Public Health , SARS-CoV-2
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