Integrated Genomic and Social Network Analyses of SARS-CoV-2 Transmission in the Healthcare Setting.

BACKGROUND: Infection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2. METHODS: We performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs. RESULTS: Among 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001). CONCLUSIONS: IP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

National trends in pediatric use of anticonvulsants.

OBJECTIVE: This research study aimed to assess national trends in pediatric use of anticonvulsants for seizures and psychiatric disorders. METHODS: In a cross-sectional design, data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey were analyzed. Outpatient visit information for youths (ages 0-17 years) was grouped by year for 1996-1997, 2000-2001, 2004-2005, and 2008-2009. Six of the most common anticonvulsant drugs used for psychiatric conditions were examined. Psychiatric diagnoses and seizure or convulsion diagnoses were identified with ICD-9-CM codes. The primary outcome measure was percentage prevalence of visits for anticonvulsants that included a psychiatric diagnosis as a proportion of total youth visits for an anticonvulsant. Total, diagnosis-stratified, and drug-specific visits, as well as visits for concomitant anticonvulsants and psychotropics, were analyzed. RESULTS: As a proportion of total youth visits for anticonvulsants, visits with a psychiatric diagnosis increased 1.7 fold (p<.001), whereas the proportion of seizure-related visits did not change significantly. Regardless of diagnosis, anticonvulsant use significantly increased, from .33% to .68% of total youth visits in the 14-year period. There were significant increases in anticonvulsant use to treat pediatric bipolar disorder and disruptive behavior disorders. Visits noting divalproex decreased while visits noting lamotrigine increased among visits involving a psychiatric diagnosis. The concomitant use of stimulants and anticonvulsants significantly increased in visits noting a psychiatric diagnosis. CONCLUSIONS: Whereas anticonvulsant use for seizure disorders across the 14-year period was stable, the use of these drugs for psychiatric conditions rose to a dominant position. The growth of concomitant and off-label use to treat behavioral disorders raises questions about effectiveness and safety in community populations of youths.