Trends in Ketamine Use, Exposures, and Seizures in the United States

2021 ◽  
pp. e1-e4
Author(s):  
Joseph J. Palamar ◽  
Caroline Rutherford ◽  
Katherine M. Keyes
Keyword(s):  
Public Health ◽  
United States ◽  
Media Coverage ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Nonmedical Use ◽  
Medical Indications

Objectives. To determine whether there have been shifts in nonmedical ketamine use, poisonings (“exposures”), and seizures. Methods. We used generalized additive models to detect trends in past-year use (2006–2019), exposures (1991–2019), and seizures (2000–2019) involving ketamine in the United States. Results. There was a quarterly increase in self-reported past-year nonmedical ketamine use in 2006 to 2014 (Β = 0.21; P = .030) and an increase in 2015 to 2019 (Β = 0.29; P = .036), reaching a peak of 0.9% in late 2019. The rate of exposures increased from 1991 through 2019 (Β = 0.87; P = .006), and there was an increase to 1.1 exposures per 1 000 000 population in 2014, with rates remaining stable through 2019. The rate of ketamine seizures increased from 2000 through 2019 (Β = 2.27; P < .001), with seizures reaching a peak in 2019 at 3.2 per 1000 seizures. Conclusions. Indicators suggest that ketamine use and availability has increased, including before increased medical indications, but nonmedical use is still currently uncommon despite increased acceptance and media coverage. (Am J Public Health. Published online ahead of print October 7, 2021:e1–e4. https://doi.org/10.2105/AJPH.2021.306486 )

scholarly journals Tracking SARS-CoV-2 Spike Protein Mutations in the United States (January 2020–March 2021) Using a Statistical Learning Strategy

Viruses ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 9
Author(s):  
Lue Ping Zhao ◽  
Terry P. Lybrand ◽  
Peter B. Gilbert ◽  
Thomas R. Hawn ◽  
Joshua T. Schiffer ◽  
...  
Keyword(s):  
United States ◽  
Unsupervised Learning ◽  
Statistical Learning ◽  
Learning Strategy ◽  
Temporal Dynamics ◽  
Generalized Additive Models ◽  
The United States ◽  
Additive Models ◽  
Spike Protein ◽  
Protein Mutations

The emergence and establishment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of interest (VOIs) and variants of concern (VOCs) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from coronavirus disease 2019 (COVID-19) cases in the United States (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from 19 January 2020 to 15 March 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics and to identify VRVs with significant and substantial dynamics (false discovery rate q-value < 0.01; maximum VRV proportion >10% on at least one day). Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modeling was performed to gain insight into the potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which had not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identified 17 VRVs ~91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of four VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported a potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

scholarly journals Spatial and temporal analysis of fatal off-piste and backcountry avalanche accidents in Austria with a comparison of results in Switzerland, France, Italy and the United States

2016 ◽  
Author(s):  
Christian Pfeifer ◽  
Peter Höller ◽  
Achim Zeileis
Keyword(s):  
United States ◽  
Generalized Additive Models ◽  
Regional Distribution ◽  
Temporal Analysis ◽  
The United States ◽  
Additive Models ◽  
Spatial And Temporal Patterns ◽  
Federal States ◽  
Increasing Trend ◽  
Avalanche Accidents

Abstract. In this article we analyzed spatial and temporal patterns of fatal Austrian avalanche accidents caused by backcountry and off-piste skiers and snowboarders within the winter periods 1967/68–2010/11. The data were based on reports of the Austrian Board for Alpine Safety and reports of the information services of the federal states. Using the date and the location of the recorded avalanche accidents we were able to carry out spatial and temporal analyses applying generalized additive models and Markov random field models. As the result of the trend analysis we noticed an increasing trend of avalanche fatalities within the winter periods from 1967/68 to 2010/11, which is in contradiction to the widespread opinion that the number of fatalities is constant over time. Additionally, we compared Austrian results with results of Switzerland, France, Italy and the United States based on data from the International Commission of Alpine Rescue (ICAR). As the result of the spatial analysis we noticed two hotspots of avalanche fatalities ("Arlberg-Silvretta" and "Sölden"). Because of the increasing trend and the rather "narrow" regional distribution of the fatalities consequences on prevention of avalanche accidents were highly recommended.

Can increases in Twitter posts predict increases in cumulative incidence of COVID-19 in the United States? Evidence that social media can inform epidemic surveillance. (Preprint)

2020 ◽  
Author(s):  
Ruoyan Sun ◽  
Henna Budhwani
Keyword(s):  
Public Health ◽  
United States ◽  
Cumulative Incidence ◽  
Hot Spots ◽  
Adult Population ◽  
State Level ◽  
The United States ◽  
Education Attainment ◽  
Public Health Systems ◽  
Per Capita

BACKGROUND Though public health systems are responding rapidly to the COVID-19 pandemic, outcomes from publicly available, crowd-sourced big data may assist in helping to identify hot spots, prioritize equipment allocation and staffing, while also informing health policy related to “shelter in place” and social distancing recommendations. OBJECTIVE To assess if the rising state-level prevalence of COVID-19 related posts on Twitter (tweets) is predictive of state-level cumulative COVID-19 incidence after controlling for socio-economic characteristics. METHODS We identified extracted COVID-19 related tweets from January 21st to March 7th (2020) across all 50 states (N = 7,427,057). Tweets were combined with state-level characteristics and confirmed COVID-19 cases to determine the association between public commentary and cumulative incidence. RESULTS The cumulative incidence of COVID-19 cases varied significantly across states. Ratio of tweet increase (p=0.03), number of physicians per 1,000 population (p=0.01), education attainment (p=0.006), income per capita (p = 0.002), and percentage of adult population (p=0.003) were positively associated with cumulative incidence. Ratio of tweet increase was significantly associated with the logarithmic of cumulative incidence (p=0.06) with a coefficient of 0.26. CONCLUSIONS An increase in the prevalence of state-level tweets was predictive of an increase in COVID-19 diagnoses, providing evidence that Twitter can be a valuable surveillance tool for public health.

Review of form 483s and warning letters to pharmaceutical manufacturers issued by USFDA

2021 ◽  
pp. 174113432110235
Author(s):  
Chandan Saini ◽  
Ashish Miglani ◽  
Pankaj Musyuni ◽  
Geeta Aggarwal
Keyword(s):  
Public Health ◽  
United States ◽  
Regulatory Authority ◽  
The United States ◽  
Federal Law ◽  
Pharmaceutical Manufacturers ◽  
The World ◽  
Considerable Impact ◽  
A Company ◽  
Responsible Person

Regular inspections are carried out to ensure system conformity by the Food and Drugs Regulatory Authority (FDA) of the United States one of the most stringent regulatory authorities in the world. The inspectors send Form 483 to the management after the inspection, detailing the inappropriate conditions. Because the FDA guidelines are difficult to comply with, a company can contravene the regulations. If any significant infringements can affect the protection, quality, effectiveness, or public health of the drug is identified, the FDA issues advice to the company. Warning Letters (WL) shall be an official notification of non-compliance with federal law within a period to be issued by manufacturer, clinician, distributor, or responsible person in the company. The delivery of a letter has a considerable impact on the company's reputation and position in the market. Inadequate WL reactions could lead to a refusal, import denial, memorandum or even conviction and order. A brief study was conducted in this document of Form 483 and WL for four years (2017–2020) on an understanding the regulatory provisions.

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